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50 Great Cybersecurity Research Paper Topics

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Students are required to write papers and essays on cyber security topics when pursuing programs in cyber security disciplines. These topics are technical and they require learners to inherently understand this subject. What’s more, students should have impeccable research and writing skills.

Additionally, students should choose cyber security topics to write their papers and essays carefully. As a science field, cyber security is developing rapidly and constantly. As such, learners can always find interesting topics to write papers and essays about.

Pick Cyber Security Topics From Our List

Software and computer administration cyber security topics.

Network Security Topic Ideas

Nevertheless, selecting cybersecurity topics for research shouldn’t be a rushed process. That’s because the chosen topics will influence the experience of students while writing and the grades they will score. Therefore, learners should focus on choosing topics that they will be comfortable researching and writing about.

If you’re having a hard time choosing the topics to research and write about, here are categories of some of the best cybersecurity paper topics that you can consider. We also advise you to check out capstone project topics .

The cyber security of a company can be compromised in many ways when it comes to software and computer administration. As such, software and computer administration is a great sources of cybersecurity research paper topics. Here are some of the best topics in this category.

These topics address issues that affect anybody or any organization that uses a computer or any device to access the internet and exchange information. As such, most people, including teachers and professors, will be impressed by papers and essays written about them.

CyberSecurity Research Paper Topics on Data Protection

Individuals and companies send and receive a lot of data every day. As such, this category has some of the best cybersecurity topics for presentation. That’s because they address issues that affect many people and organizations. Here are some of the best information security topics to consider when writing papers and essays or preparing a presentation.

Every computer or internet user wants to be sure that their data is safe and protected. Papers and essays that are written on these topics address issues of data protection. As such, many people will find them worth reading.

CyberSecurity Awareness Research Paper Topics

The best cyber security topics for research papers do more than just address a single issue. They also inform the readers. Here are some of the best cyber security topics for research papers that also focus on creating awareness.

Many people are not aware of many things that affect their cyber security. These topics are relevant because they enhance the awareness of the internet and computer users.

Most organizations today have networked systems that enhance their operations. Unfortunately, criminals have learned to target those networked systems with their criminal activities. As such, students can address some of these issues with their cyber security thesis topics. Here are interesting topics that learners can research and write about in this category.

These computer security topics can be used to write papers and essays for college or even commissioned by organizations and used for presentation purposes.

Current and Interesting Topics in CyberSecurity

Some computer security research topics seek to address issues that affect society at the moment. Here are examples of such topics.

Such technology security topics are trendy because they address issues that affect most people in modern society. Nevertheless, students should conduct extensive research to draft solid papers and essays on these topics.

This cyber security topic list is not exhaustive. You can contact our thesis writers if you need more ideas or help. Students have many topics to consider depending on their academic programs, interests, and instructions provided by educators or professors. Nevertheless, students should focus on choosing topics that will enable them to come up with informative and comprehensive papers. Thus, every student should choose an information security topic for which they can find relevant and supporting data.

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Cybersecurity data science: an overview from machine learning perspective

Journal of Big Data volume  7 , Article number:  41 ( 2020 ) Cite this article

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In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model , is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss on cybersecurity data science , where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions . Furthermore, we provide a machine learning based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.


Due to the increasing dependency on digitalization and Internet-of-Things (IoT) [ 1 ], various security incidents such as unauthorized access [ 2 ], malware attack [ 3 ], zero-day attack [ 4 ], data breach [ 5 ], denial of service (DoS) [ 2 ], social engineering or phishing [ 6 ] etc. have grown at an exponential rate in recent years. For instance, in 2010, there were less than 50 million unique malware executables known to the security community. By 2012, they were double around 100 million, and in 2019, there are more than 900 million malicious executables known to the security community, and this number is likely to grow, according to the statistics of AV-TEST institute in Germany [ 7 ]. Cybercrime and attacks can cause devastating financial losses and affect organizations and individuals as well. It’s estimated that, a data breach costs 8.19 million USD for the United States and 3.9 million USD on an average [ 8 ], and the annual cost to the global economy from cybercrime is 400 billion USD [ 9 ]. According to Juniper Research [ 10 ], the number of records breached each year to nearly triple over the next 5 years. Thus, it’s essential that organizations need to adopt and implement a strong cybersecurity approach to mitigate the loss. According to [ 11 ], the national security of a country depends on the business, government, and individual citizens having access to applications and tools which are highly secure, and the capability on detecting and eliminating such cyber-threats in a timely way. Therefore, to effectively identify various cyber incidents either previously seen or unseen, and intelligently protect the relevant systems from such cyber-attacks, is a key issue to be solved urgently.

figure 1

Popularity trends of data science, machine learning and cybersecurity over time, where x-axis represents the timestamp information and y axis represents the corresponding popularity values

Cybersecurity is a set of technologies and processes designed to protect computers, networks, programs and data from attack, damage, or unauthorized access [ 12 ]. In recent days, cybersecurity is undergoing massive shifts in technology and its operations in the context of computing, and data science (DS) is driving the change, where machine learning (ML), a core part of “Artificial Intelligence” (AI) can play a vital role to discover the insights from data. Machine learning can significantly change the cybersecurity landscape and data science is leading a new scientific paradigm [ 13 , 14 ]. The popularity of these related technologies is increasing day-by-day, which is shown in Fig.  1 , based on the data of the last five years collected from Google Trends [ 15 ]. The figure represents timestamp information in terms of a particular date in the x-axis and corresponding popularity in the range of 0 (minimum) to 100 (maximum) in the y-axis. As shown in Fig.  1 , the popularity indication values of these areas are less than 30 in 2014, while they exceed 70 in 2019, i.e., more than double in terms of increased popularity. In this paper, we focus on cybersecurity data science (CDS), which is broadly related to these areas in terms of security data processing techniques and intelligent decision making in real-world applications. Overall, CDS is security data-focused, applies machine learning methods to quantify cyber risks, and ultimately seeks to optimize cybersecurity operations. Thus, the purpose of this paper is for those academia and industry people who want to study and develop a data-driven smart cybersecurity model based on machine learning techniques. Therefore, great emphasis is placed on a thorough description of various types of machine learning methods, and their relations and usage in the context of cybersecurity. This paper does not describe all of the different techniques used in cybersecurity in detail; instead, it gives an overview of cybersecurity data science modeling based on artificial intelligence, particularly from machine learning perspective.

The ultimate goal of cybersecurity data science is data-driven intelligent decision making from security data for smart cybersecurity solutions. CDS represents a partial paradigm shift from traditional well-known security solutions such as firewalls, user authentication and access control, cryptography systems etc. that might not be effective according to today’s need in cyber industry [ 16 , 17 , 18 , 19 ]. The problems are these are typically handled statically by a few experienced security analysts, where data management is done in an ad-hoc manner [ 20 , 21 ]. However, as an increasing number of cybersecurity incidents in different formats mentioned above continuously appear over time, such conventional solutions have encountered limitations in mitigating such cyber risks. As a result, numerous advanced attacks are created and spread very quickly throughout the Internet. Although several researchers use various data analysis and learning techniques to build cybersecurity models that are summarized in “ Machine learning tasks in cybersecurity ” section, a comprehensive security model based on the effective discovery of security insights and latest security patterns could be more useful. To address this issue, we need to develop more flexible and efficient security mechanisms that can respond to threats and to update security policies to mitigate them intelligently in a timely manner. To achieve this goal, it is inherently required to analyze a massive amount of relevant cybersecurity data generated from various sources such as network and system sources, and to discover insights or proper security policies with minimal human intervention in an automated manner.

Analyzing cybersecurity data and building the right tools and processes to successfully protect against cybersecurity incidents goes beyond a simple set of functional requirements and knowledge about risks, threats or vulnerabilities. For effectively extracting the insights or the patterns of security incidents, several machine learning techniques, such as feature engineering, data clustering, classification, and association analysis, or neural network-based deep learning techniques can be used, which are briefly discussed in “ Machine learning tasks in cybersecurity ” section. These learning techniques are capable to find the anomalies or malicious behavior and data-driven patterns of associated security incidents to make an intelligent decision. Thus, based on the concept of data-driven decision making, we aim to focus on cybersecurity data science , where the data is being gathered from relevant cybersecurity sources such as network activity, database activity, application activity, or user activity, and the analytics complement the latest data-driven patterns for providing corresponding security solutions.

The contributions of this paper are summarized as follows.

We first make a brief discussion on the concept of cybersecurity data science and relevant methods to understand its applicability towards data-driven intelligent decision making in the domain of cybersecurity. For this purpose, we also make a review and brief discussion on different machine learning tasks in cybersecurity, and summarize various cybersecurity datasets highlighting their usage in different data-driven cyber applications.

We then discuss and summarize a number of associated research issues and future directions in the area of cybersecurity data science, that could help both the academia and industry people to further research and development in relevant application areas.

Finally, we provide a generic multi-layered framework of the cybersecurity data science model based on machine learning techniques. In this framework, we briefly discuss how the cybersecurity data science model can be used to discover useful insights from security data and making data-driven intelligent decisions to build smart cybersecurity systems.

The remainder of the paper is organized as follows. “ Background ” section summarizes background of our study and gives an overview of the related technologies of cybersecurity data science. “ Cybersecurity data science ” section defines and discusses briefly about cybersecurity data science including various categories of cyber incidents data. In “  Machine learning tasks in cybersecurity ” section, we briefly discuss various categories of machine learning techniques including their relations with cybersecurity tasks and summarize a number of machine learning based cybersecurity models in the field. “ Research issues and future directions ” section briefly discusses and highlights various research issues and future directions in the area of cybersecurity data science. In “  A multi-layered framework for smart cybersecurity services ” section, we suggest a machine learning-based framework to build cybersecurity data science model and discuss various layers with their roles. In “  Discussion ” section, we highlight several key points regarding our studies. Finally,  “ Conclusion ” section concludes this paper.

In this section, we give an overview of the related technologies of cybersecurity data science including various types of cybersecurity incidents and defense strategies.

Over the last half-century, the information and communication technology (ICT) industry has evolved greatly, which is ubiquitous and closely integrated with our modern society. Thus, protecting ICT systems and applications from cyber-attacks has been greatly concerned by the security policymakers in recent days [ 22 ]. The act of protecting ICT systems from various cyber-threats or attacks has come to be known as cybersecurity [ 9 ]. Several aspects are associated with cybersecurity: measures to protect information and communication technology; the raw data and information it contains and their processing and transmitting; associated virtual and physical elements of the systems; the degree of protection resulting from the application of those measures; and eventually the associated field of professional endeavor [ 23 ]. Craigen et al. defined “cybersecurity as a set of tools, practices, and guidelines that can be used to protect computer networks, software programs, and data from attack, damage, or unauthorized access” [ 24 ]. According to Aftergood et al. [ 12 ], “cybersecurity is a set of technologies and processes designed to protect computers, networks, programs and data from attacks and unauthorized access, alteration, or destruction”. Overall, cybersecurity concerns with the understanding of diverse cyber-attacks and devising corresponding defense strategies that preserve several properties defined as below [ 25 , 26 ].

Confidentiality is a property used to prevent the access and disclosure of information to unauthorized individuals, entities or systems.

Integrity is a property used to prevent any modification or destruction of information in an unauthorized manner.

Availability is a property used to ensure timely and reliable access of information assets and systems to an authorized entity.

The term cybersecurity applies in a variety of contexts, from business to mobile computing, and can be divided into several common categories. These are - network security that mainly focuses on securing a computer network from cyber attackers or intruders; application security that takes into account keeping the software and the devices free of risks or cyber-threats; information security that mainly considers security and the privacy of relevant data; operational security that includes the processes of handling and protecting data assets. Typical cybersecurity systems are composed of network security systems and computer security systems containing a firewall, antivirus software, or an intrusion detection system [ 27 ].

Cyberattacks and security risks

The risks typically associated with any attack, which considers three security factors, such as threats, i.e., who is attacking, vulnerabilities, i.e., the weaknesses they are attacking, and impacts, i.e., what the attack does [ 9 ]. A security incident is an act that threatens the confidentiality, integrity, or availability of information assets and systems. Several types of cybersecurity incidents that may result in security risks on an organization’s systems and networks or an individual [ 2 ]. These are:

Unauthorized access that describes the act of accessing information to network, systems or data without authorization that results in a violation of a security policy [ 2 ];

Malware known as malicious software, is any program or software that intentionally designed to cause damage to a computer, client, server, or computer network, e.g., botnets. Examples of different types of malware including computer viruses, worms, Trojan horses, adware, ransomware, spyware, malicious bots, etc. [ 3 , 26 ]; Ransom malware, or ransomware , is an emerging form of malware that prevents users from accessing their systems or personal files, or the devices, then demands an anonymous online payment in order to restore access.

Denial-of-Service is an attack meant to shut down a machine or network, making it inaccessible to its intended users by flooding the target with traffic that triggers a crash. The Denial-of-Service (DoS) attack typically uses one computer with an Internet connection, while distributed denial-of-service (DDoS) attack uses multiple computers and Internet connections to flood the targeted resource [ 2 ];

Phishing a type of social engineering , used for a broad range of malicious activities accomplished through human interactions, in which the fraudulent attempt takes part to obtain sensitive information such as banking and credit card details, login credentials, or personally identifiable information by disguising oneself as a trusted individual or entity via an electronic communication such as email, text, or instant message, etc. [ 26 ];

Zero-day attack is considered as the term that is used to describe the threat of an unknown security vulnerability for which either the patch has not been released or the application developers were unaware [ 4 , 28 ].

Beside these attacks mentioned above, privilege escalation [ 29 ], password attack [ 30 ], insider threat [ 31 ], man-in-the-middle [ 32 ], advanced persistent threat [ 33 ], SQL injection attack [ 34 ], cryptojacking attack [ 35 ], web application attack [ 30 ] etc. are well-known as security incidents in the field of cybersecurity. A data breach is another type of security incident, known as a data leak, which is involved in the unauthorized access of data by an individual, application, or service [ 5 ]. Thus, all data breaches are considered as security incidents, however, all the security incidents are not data breaches. Most data breaches occur in the banking industry involving the credit card numbers, personal information, followed by the healthcare sector and the public sector [ 36 ].

Cybersecurity defense strategies

Defense strategies are needed to protect data or information, information systems, and networks from cyber-attacks or intrusions. More granularly, they are responsible for preventing data breaches or security incidents and monitoring and reacting to intrusions, which can be defined as any kind of unauthorized activity that causes damage to an information system [ 37 ]. An intrusion detection system (IDS) is typically represented as “a device or software application that monitors a computer network or systems for malicious activity or policy violations” [ 38 ]. The traditional well-known security solutions such as anti-virus, firewalls, user authentication, access control, data encryption and cryptography systems, however might not be effective according to today’s need in the cyber industry

[ 16 , 17 , 18 , 19 ]. On the other hand, IDS resolves the issues by analyzing security data from several key points in a computer network or system [ 39 , 40 ]. Moreover, intrusion detection systems can be used to detect both internal and external attacks.

Intrusion detection systems are different categories according to the usage scope. For instance, a host-based intrusion detection system (HIDS), and network intrusion detection system (NIDS) are the most common types based on the scope of single computers to large networks. In a HIDS, the system monitors important files on an individual system, while it analyzes and monitors network connections for suspicious traffic in a NIDS. Similarly, based on methodologies, the signature-based IDS, and anomaly-based IDS are the most well-known variants [ 37 ].

Signature-based IDS : A signature can be a predefined string, pattern, or rule that corresponds to a known attack. A particular pattern is identified as the detection of corresponding attacks in a signature-based IDS. An example of a signature can be known patterns or a byte sequence in a network traffic, or sequences used by malware. To detect the attacks, anti-virus software uses such types of sequences or patterns as a signature while performing the matching operation. Signature-based IDS is also known as knowledge-based or misuse detection [ 41 ]. This technique can be efficient to process a high volume of network traffic, however, is strictly limited to the known attacks only. Thus, detecting new attacks or unseen attacks is one of the biggest challenges faced by this signature-based system.

Anomaly-based IDS : The concept of anomaly-based detection overcomes the issues of signature-based IDS discussed above. In an anomaly-based intrusion detection system, the behavior of the network is first examined to find dynamic patterns, to automatically create a data-driven model, to profile the normal behavior, and thus it detects deviations in the case of any anomalies [ 41 ]. Thus, anomaly-based IDS can be treated as a dynamic approach, which follows behavior-oriented detection. The main advantage of anomaly-based IDS is the ability to identify unknown or zero-day attacks [ 42 ]. However, the issue is that the identified anomaly or abnormal behavior is not always an indicator of intrusions. It sometimes may happen because of several factors such as policy changes or offering a new service.

In addition, a hybrid detection approach [ 43 , 44 ] that takes into account both the misuse and anomaly-based techniques discussed above can be used to detect intrusions. In a hybrid system, the misuse detection system is used for detecting known types of intrusions and anomaly detection system is used for novel attacks [ 45 ]. Beside these approaches, stateful protocol analysis can also be used to detect intrusions that identifies deviations of protocol state similarly to the anomaly-based method, however it uses predetermined universal profiles based on accepted definitions of benign activity [ 41 ]. In Table 1 , we have summarized these common approaches highlighting their pros and cons. Once the detecting has been completed, the intrusion prevention system (IPS) that is intended to prevent malicious events, can be used to mitigate the risks in different ways such as manual, providing notification, or automatic process [ 46 ]. Among these approaches, an automatic response system could be more effective as it does not involve a human interface between the detection and response systems.

We are living in the age of data, advanced analytics, and data science, which are related to data-driven intelligent decision making. Although, the process of searching patterns or discovering hidden and interesting knowledge from data is known as data mining [ 47 ], in this paper, we use the broader term “data science” rather than data mining. The reason is that, data science, in its most fundamental form, is all about understanding of data. It involves studying, processing, and extracting valuable insights from a set of information. In addition to data mining, data analytics is also related to data science. The development of data mining, knowledge discovery, and machine learning that refers creating algorithms and program which learn on their own, together with the original data analysis and descriptive analytics from the statistical perspective, forms the general concept of “data analytics” [ 47 ]. Nowadays, many researchers use the term “data science” to describe the interdisciplinary field of data collection, preprocessing, inferring, or making decisions by analyzing the data. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. According to Cao et al. [ 47 ] “data science is a new interdisciplinary field that synthesizes and builds on statistics, informatics, computing, communication, management, and sociology to study data and its environments, to transform data to insights and decisions by following a data-to-knowledge-to-wisdom thinking and methodology”. As a high-level statement in the context of cybersecurity, we can conclude that it is the study of security data to provide data-driven solutions for the given security problems, as known as “the science of cybersecurity data”. Figure 2 shows the typical data-to-insight-to-decision transfer at different periods and general analytic stages in data science, in terms of a variety of analytics goals (G) and approaches (A) to achieve the data-to-decision goal [ 47 ].

figure 2

Data-to-insight-to-decision analytic stages in data science [ 47 ]

Based on the analytic power of data science including machine learning techniques, it can be a viable component of security strategies. By using data science techniques, security analysts can manipulate and analyze security data more effectively and efficiently, uncovering valuable insights from data. Thus, data science methodologies including machine learning techniques can be well utilized in the context of cybersecurity, in terms of problem understanding, gathering security data from diverse sources, preparing data to feed into the model, data-driven model building and updating, for providing smart security services, which motivates to define cybersecurity data science and to work in this research area.

Cybersecurity data science

In this section, we briefly discuss cybersecurity data science including various categories of cyber incidents data with the usage in different application areas, and the key terms and areas related to our study.

Understanding cybersecurity data

Data science is largely driven by the availability of data [ 48 ]. Datasets typically represent a collection of information records that consist of several attributes or features and related facts, in which cybersecurity data science is based on. Thus, it’s important to understand the nature of cybersecurity data containing various types of cyberattacks and relevant features. The reason is that raw security data collected from relevant cyber sources can be used to analyze the various patterns of security incidents or malicious behavior, to build a data-driven security model to achieve our goal. Several datasets exist in the area of cybersecurity including intrusion analysis, malware analysis, anomaly, fraud, or spam analysis that are used for various purposes. In Table 2 , we summarize several such datasets including their various features and attacks that are accessible on the Internet, and highlight their usage based on machine learning techniques in different cyber applications. Effectively analyzing and processing of these security features, building target machine learning-based security model according to the requirements, and eventually, data-driven decision making, could play a role to provide intelligent cybersecurity services that are discussed briefly in “ A multi-layered framework for smart cybersecurity services ” section.

Defining cybersecurity data science

Data science is transforming the world’s industries. It is critically important for the future of intelligent cybersecurity systems and services because of “security is all about data”. When we seek to detect cyber threats, we are analyzing the security data in the form of files, logs, network packets, or other relevant sources. Traditionally, security professionals didn’t use data science techniques to make detections based on these data sources. Instead, they used file hashes, custom-written rules like signatures, or manually defined heuristics [ 21 ]. Although these techniques have their own merits in several cases, it needs too much manual work to keep up with the changing cyber threat landscape. On the contrary, data science can make a massive shift in technology and its operations, where machine learning algorithms can be used to learn or extract insight of security incident patterns from the training data for their detection and prevention. For instance, to detect malware or suspicious trends, or to extract policy rules, these techniques can be used.

In recent days, the entire security industry is moving towards data science, because of its capability to transform raw data into decision making. To do this, several data-driven tasks can be associated, such as—(i) data engineering focusing practical applications of data gathering and analysis; (ii) reducing data volume that deals with filtering significant and relevant data to further analysis; (iii) discovery and detection that focuses on extracting insight or incident patterns or knowledge from data; (iv) automated models that focus on building data-driven intelligent security model; (v) targeted security  alerts focusing on the generation of remarkable security alerts based on discovered knowledge that minimizes the false alerts, and (vi) resource optimization that deals with the available resources to achieve the target goals in a security system. While making data-driven decisions, behavioral analysis could also play a significant role in the domain of cybersecurity [ 81 ].

Thus, the concept of cybersecurity data science incorporates the methods and techniques of data science and machine learning as well as the behavioral analytics of various security incidents. The combination of these technologies has given birth to the term “cybersecurity data science”, which refers to collect a large amount of security event data from different sources and analyze it using machine learning technologies for detecting security risks or attacks either through the discovery of useful insights or the latest data-driven patterns. It is, however, worth remembering that cybersecurity data science is not just about a collection of machine learning algorithms, rather,  a process that can help security professionals or analysts to scale and automate their security activities in a smart way and in a timely manner. Therefore, the formal definition can be as follows: “Cybersecurity data science is a research or working area existing at the intersection of cybersecurity, data science, and machine learning or artificial intelligence, which is mainly security data-focused, applies machine learning methods, attempts to quantify cyber-risks or incidents, and promotes inferential techniques to analyze behavioral patterns in security data. It also focuses on generating security response alerts, and eventually seeks for optimizing cybersecurity solutions, to build automated and intelligent cybersecurity systems.”

Table  3 highlights some key terms associated with cybersecurity data science. Overall, the outputs of cybersecurity data science are typically security data products, which can be a data-driven security model, policy rule discovery, risk or attack prediction, potential security service and recommendation, or the corresponding security system depending on the given security problem in the domain of cybersecurity. In the next section, we briefly discuss various machine learning tasks with examples within the scope of our study.

Machine learning tasks in cybersecurity

Machine learning (ML) is typically considered as a branch of “Artificial Intelligence”, which is closely related to computational statistics, data mining and analytics, data science, particularly focusing on making the computers to learn from data [ 82 , 83 ]. Thus, machine learning models typically comprise of a set of rules, methods, or complex “transfer functions” that can be applied to find interesting data patterns, or to recognize or predict behavior [ 84 ], which could play an important role in the area of cybersecurity. In the following, we discuss different methods that can be used to solve machine learning tasks and how they are related to cybersecurity tasks.

Supervised learning

Supervised learning is performed when specific targets are defined to reach from a certain set of inputs, i.e., task-driven approach. In the area of machine learning, the most popular supervised learning techniques are known as classification and regression methods [ 129 ]. These techniques are popular to classify or predict the future for a particular security problem. For instance, to predict denial-of-service attack (yes, no) or to identify different classes of network attacks such as scanning and spoofing, classification techniques can be used in the cybersecurity domain. ZeroR [ 83 ], OneR [ 130 ], Navies Bayes [ 131 ], Decision Tree [ 132 , 133 ], K-nearest neighbors [ 134 ], support vector machines [ 135 ], adaptive boosting [ 136 ], and logistic regression [ 137 ] are the well-known classification techniques. In addition, recently Sarker et al. have proposed BehavDT [ 133 ], and IntruDtree [ 106 ] classification techniques that are able to effectively build a data-driven predictive model. On the other hand, to predict the continuous or numeric value, e.g., total phishing attacks in a certain period or predicting the network packet parameters, regression techniques are useful. Regression analyses can also be used to detect the root causes of cybercrime and other types of fraud [ 138 ]. Linear regression [ 82 ], support vector regression [ 135 ] are the popular regression techniques. The main difference between classification and regression is that the output variable in the regression is numerical or continuous, while the predicted output for classification is categorical or discrete. Ensemble learning is an extension of supervised learning while mixing different simple models, e.g., Random Forest learning [ 139 ] that generates multiple decision trees to solve a particular security task.

Unsupervised learning

In unsupervised learning problems, the main task is to find patterns, structures, or knowledge in unlabeled data, i.e., data-driven approach [ 140 ]. In the area of cybersecurity, cyber-attacks like malware stays hidden in some ways, include changing their behavior dynamically and autonomously to avoid detection. Clustering techniques, a type of unsupervised learning, can help to uncover the hidden patterns and structures from the datasets, to identify indicators of such sophisticated attacks. Similarly, in identifying anomalies, policy violations, detecting, and eliminating noisy instances in data, clustering techniques can be useful. K-means [ 141 ], K-medoids [ 142 ] are the popular partitioning clustering algorithms, and single linkage [ 143 ] or complete linkage [ 144 ] are the well-known hierarchical clustering algorithms used in various application domains. Moreover, a bottom-up clustering approach proposed by Sarker et al. [ 145 ] can also be used by taking into account the data characteristics.

Besides, feature engineering tasks like optimal feature selection or extraction related to a particular security problem could be useful for further analysis [ 106 ]. Recently, Sarker et al. [ 106 ] have proposed an approach for selecting security features according to their importance score values. Moreover, Principal component analysis, linear discriminant analysis, pearson correlation analysis, or non-negative matrix factorization are the popular dimensionality reduction techniques to solve such issues [ 82 ]. Association rule learning is another example, where machine learning based policy rules can prevent cyber-attacks. In an expert system, the rules are usually manually defined by a knowledge engineer working in collaboration with a domain expert [ 37 , 140 , 146 ]. Association rule learning on the contrary, is the discovery of rules or relationships among a set of available security features or attributes in a given dataset [ 147 ]. To quantify the strength of relationships, correlation analysis can be used [ 138 ]. Many association rule mining algorithms have been proposed in the area of machine learning and data mining literature, such as logic-based [ 148 ], frequent pattern based [ 149 , 150 , 151 ], tree-based [ 152 ], etc. Recently, Sarker et al. [ 153 ] have proposed an association rule learning approach considering non-redundant generation, that can be used to discover a set of useful security policy rules. Moreover, AIS [ 147 ], Apriori [ 149 ], Apriori-TID and Apriori-Hybrid [ 149 ], FP-Tree [ 152 ], and RARM [ 154 ], and Eclat [ 155 ] are the well-known association rule learning algorithms that are capable to solve such problems by generating a set of policy rules in the domain of cybersecurity.

Neural networks and deep learning

Deep learning is a part of machine learning in the area of artificial intelligence, which is a computational model that is inspired by the biological neural networks in the human brain [ 82 ]. Artificial Neural Network (ANN) is frequently used in deep learning and the most popular neural network algorithm is backpropagation [ 82 ]. It performs learning on a multi-layer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer. The main difference between deep learning and classical machine learning is its performance on the amount of security data increases. Typically deep learning algorithms perform well when the data volumes are large, whereas machine learning algorithms perform comparatively better on small datasets [ 44 ]. In our earlier work, Sarker et al. [ 129 ], we have illustrated the effectiveness of these approaches considering contextual datasets. However, deep learning approaches mimic the human brain mechanism to interpret large amount of data or the complex data such as images, sounds and texts [ 44 , 129 ]. In terms of feature extraction to build models, deep learning reduces the effort of designing a feature extractor for each problem than the classical machine learning techniques. Beside these characteristics, deep learning typically takes a long time to train an algorithm than a machine learning algorithm, however, the test time is exactly the opposite [ 44 ]. Thus, deep learning relies more on high-performance machines with GPUs than classical machine-learning algorithms [ 44 , 156 ]. The most popular deep neural network learning models include multi-layer perceptron (MLP) [ 157 ], convolutional neural network (CNN) [ 158 ], recurrent neural network (RNN) or long-short term memory (LSTM) network [ 121 , 158 ]. In recent days, researchers use these deep learning techniques for different purposes such as detecting network intrusions, malware traffic detection and classification, etc. in the domain of cybersecurity [ 44 , 159 ].

Other learning techniques

Semi-supervised learning can be described as a hybridization of supervised and unsupervised techniques discussed above, as it works on both the labeled and unlabeled data. In the area of cybersecurity, it could be useful, when it requires to label data automatically without human intervention, to improve the performance of cybersecurity models. Reinforcement techniques are another type of machine learning that characterizes an agent by creating its own learning experiences through interacting directly with the environment, i.e., environment-driven approach, where the environment is typically formulated as a Markov decision process and take decision based on a reward function [ 160 ]. Monte Carlo learning, Q-learning, Deep Q Networks, are the most common reinforcement learning algorithms [ 161 ]. For instance, in a recent work [ 126 ], the authors present an approach for detecting botnet traffic or malicious cyber activities using reinforcement learning combining with neural network classifier. In another work [ 128 ], the authors discuss about the application of deep reinforcement learning to intrusion detection for supervised problems, where they received the best results for the Deep Q-Network algorithm. In the context of cybersecurity, genetic algorithms that use fitness, selection, crossover, and mutation for finding optimization, could also be used to solve a similar class of learning problems [ 119 ].

Various types of machine learning techniques discussed above can be useful in the domain of cybersecurity, to build an effective security model. In Table  4 , we have summarized several machine learning techniques that are used to build various types of security models for various purposes. Although these models typically represent a learning-based security model, in this paper, we aim to focus on a comprehensive cybersecurity data science model and relevant issues, in order to build a data-driven intelligent security system. In the next section, we highlight several research issues and potential solutions in the area of cybersecurity data science.

Research issues and future directions

Our study opens several research issues and challenges in the area of cybersecurity data science to extract insight from relevant data towards data-driven intelligent decision making for cybersecurity solutions. In the following, we summarize these challenges ranging from data collection to decision making.

Cybersecurity datasets : Source datasets are the primary component to work in the area of cybersecurity data science. Most of the existing datasets are old and might insufficient in terms of understanding the recent behavioral patterns of various cyber-attacks. Although the data can be transformed into a meaningful understanding level after performing several processing tasks, there is still a lack of understanding of the characteristics of recent attacks and their patterns of happening. Thus, further processing or machine learning algorithms may provide a low accuracy rate for making the target decisions. Therefore, establishing a large number of recent datasets for a particular problem domain like cyber risk prediction or intrusion detection is needed, which could be one of the major challenges in cybersecurity data science.

Handling quality problems in cybersecurity datasets : The cyber datasets might be noisy, incomplete, insignificant, imbalanced, or may contain inconsistency instances related to a particular security incident. Such problems in a data set may affect the quality of the learning process and degrade the performance of the machine learning-based models [ 162 ]. To make a data-driven intelligent decision for cybersecurity solutions, such problems in data is needed to deal effectively before building the cyber models. Therefore, understanding such problems in cyber data and effectively handling such problems using existing algorithms or newly proposed algorithm for a particular problem domain like malware analysis or intrusion detection and prevention is needed, which could be another research issue in cybersecurity data science.

Security policy rule generation : Security policy rules reference security zones and enable a user to allow, restrict, and track traffic on the network based on the corresponding user or user group, and service, or the application. The policy rules including the general and more specific rules are compared against the incoming traffic in sequence during the execution, and the rule that matches the traffic is applied. The policy rules used in most of the cybersecurity systems are static and generated by human expertise or ontology-based [ 163 , 164 ]. Although, association rule learning techniques produce rules from data, however, there is a problem of redundancy generation [ 153 ] that makes the policy rule-set complex. Therefore, understanding such problems in policy rule generation and effectively handling such problems using existing algorithms or newly proposed algorithm for a particular problem domain like access control [ 165 ] is needed, which could be another research issue in cybersecurity data science.

Hybrid learning method : Most commercial products in the cybersecurity domain contain signature-based intrusion detection techniques [ 41 ]. However, missing features or insufficient profiling can cause these techniques to miss unknown attacks. In that case, anomaly-based detection techniques or hybrid technique combining signature-based and anomaly-based can be used to overcome such issues. A hybrid technique combining multiple learning techniques or a combination of deep learning and machine-learning methods can be used to extract the target insight for a particular problem domain like intrusion detection, malware analysis, access control, etc. and make the intelligent decision for corresponding cybersecurity solutions.

Protecting the valuable security information : Another issue of a cyber data attack is the loss of extremely valuable data and information, which could be damaging for an organization. With the use of encryption or highly complex signatures, one can stop others from probing into a dataset. In such cases, cybersecurity data science can be used to build a data-driven impenetrable protocol to protect such security information. To achieve this goal, cyber analysts can develop algorithms by analyzing the history of cyberattacks to detect the most frequently targeted chunks of data. Thus, understanding such data protecting problems and designing corresponding algorithms to effectively handling these problems, could be another research issue in the area of cybersecurity data science.

Context-awareness in cybersecurity : Existing cybersecurity work mainly originates from the relevant cyber data containing several low-level features. When data mining and machine learning techniques are applied to such datasets, a related pattern can be identified that describes it properly. However, a broader contextual information [ 140 , 145 , 166 ] like temporal, spatial, relationship among events or connections, dependency can be used to decide whether there exists a suspicious activity or not. For instance, some approaches may consider individual connections as DoS attacks, while security experts might not treat them as malicious by themselves. Thus, a significant limitation of existing cybersecurity work is the lack of using the contextual information for predicting risks or attacks. Therefore, context-aware adaptive cybersecurity solutions could be another research issue in cybersecurity data science.

Feature engineering in cybersecurity : The efficiency and effectiveness of a machine learning-based security model has always been a major challenge due to the high volume of network data with a large number of traffic features. The large dimensionality of data has been addressed using several techniques such as principal component analysis (PCA) [ 167 ], singular value decomposition (SVD) [ 168 ] etc. In addition to low-level features in the datasets, the contextual relationships between suspicious activities might be relevant. Such contextual data can be stored in an ontology or taxonomy for further processing. Thus how to effectively select the optimal features or extract the significant features considering both the low-level features as well as the contextual features, for effective cybersecurity solutions could be another research issue in cybersecurity data science.

Remarkable security alert generation and prioritizing : In many cases, the cybersecurity system may not be well defined and may cause a substantial number of false alarms that are unexpected in an intelligent system. For instance, an IDS deployed in a real-world network generates around nine million alerts per day [ 169 ]. A network-based intrusion detection system typically looks at the incoming traffic for matching the associated patterns to detect risks, threats or vulnerabilities and generate security alerts. However, to respond to each such alert might not be effective as it consumes relatively huge amounts of time and resources, and consequently may result in a self-inflicted DoS. To overcome this problem, a high-level management is required that correlate the security alerts considering the current context and their logical relationship including their prioritization before reporting them to users, which could be another research issue in cybersecurity data science.

Recency analysis in cybersecurity solutions : Machine learning-based security models typically use a large amount of static data to generate data-driven decisions. Anomaly detection systems rely on constructing such a model considering normal behavior and anomaly, according to their patterns. However, normal behavior in a large and dynamic security system is not well defined and it may change over time, which can be considered as an incremental growing of dataset. The patterns in incremental datasets might be changed in several cases. This often results in a substantial number of false alarms known as false positives. Thus, a recent malicious behavioral pattern is more likely to be interesting and significant than older ones for predicting unknown attacks. Therefore, effectively using the concept of recency analysis [ 170 ] in cybersecurity solutions could be another issue in cybersecurity data science.

The most important work for an intelligent cybersecurity system is to develop an effective framework that supports data-driven decision making. In such a framework, we need to consider advanced data analysis based on machine learning techniques, so that the framework is capable to minimize these issues and to provide automated and intelligent security services. Thus, a well-designed security framework for cybersecurity data and the experimental evaluation is a very important direction and a big challenge as well. In the next section, we suggest and discuss a data-driven cybersecurity framework based on machine learning techniques considering multiple processing layers.

A multi-layered framework for smart cybersecurity services

As discussed earlier, cybersecurity data science is data-focused, applies machine learning methods, attempts to quantify cyber risks, promotes inferential techniques to analyze behavioral patterns, focuses on generating security response alerts, and eventually seeks for optimizing cybersecurity operations. Hence, we briefly discuss a multiple data processing layered framework that potentially can be used to discover security insights from the raw data to build smart cybersecurity systems, e.g., dynamic policy rule-based access control or intrusion detection and prevention system. To make a data-driven intelligent decision in the resultant cybersecurity system, understanding the security problems and the nature of corresponding security data and their vast analysis is needed. For this purpose, our suggested framework not only considers the machine learning techniques to build the security model but also takes into account the incremental learning and dynamism to keep the model up-to-date and corresponding response generation, which could be more effective and intelligent for providing the expected services. Figure 3 shows an overview of the framework, involving several processing layers, from raw security event data to services. In the following, we briefly discuss the working procedure of the framework.

figure 3

A generic multi-layered framework based on machine learning techniques for smart cybersecurity services

Security data collecting

Collecting valuable cybersecurity data is a crucial step, which forms a connecting link between security problems in cyberinfrastructure and corresponding data-driven solution steps in this framework, shown in Fig.  3 . The reason is that cyber data can serve as the source for setting up ground truth of the security model that affect the model performance. The quality and quantity of cyber data decide the feasibility and effectiveness of solving the security problem according to our goal. Thus, the concern is how to collect valuable and unique needs data for building the data-driven security models.

The general step to collect and manage security data from diverse data sources is based on a particular security problem and project within the enterprise. Data sources can be classified into several broad categories such as network, host, and hybrid [ 171 ]. Within the network infrastructure, the security system can leverage different types of security data such as IDS logs, firewall logs, network traffic data, packet data, and honeypot data, etc. for providing the target security services. For instance, a given IP is considered malicious or not, could be detected by performing data analysis utilizing the data of IP addresses and their cyber activities. In the domain of cybersecurity, the network source mentioned above is considered as the primary security event source to analyze. In the host category, it collects data from an organization’s host machines, where the data sources can be operating system logs, database access logs, web server logs, email logs, application logs, etc. Collecting data from both the network and host machines are considered a hybrid category. Overall, in a data collection layer the network activity, database activity, application activity, and user activity can be the possible security event sources in the context of cybersecurity data science.

Security data preparing

After collecting the raw security data from various sources according to the problem domain discussed above, this layer is responsible to prepare the raw data for building the model by applying various necessary processes. However, not all of the collected data contributes to the model building process in the domain of cybersecurity [ 172 ]. Therefore, the useless data should be removed from the rest of the data captured by the network sniffer. Moreover, data might be noisy, have missing or corrupted values, or have attributes of widely varying types and scales. High quality of data is necessary for achieving higher accuracy in a data-driven model, which is a process of learning a function that maps an input to an output based on example input-output pairs. Thus, it might require a procedure for data cleaning, handling missing or corrupted values. Moreover, security data features or attributes can be in different types, such as continuous, discrete, or symbolic [ 106 ]. Beyond a solid understanding of these types of data and attributes and their permissible operations, its need to preprocess the data and attributes to convert into the target type. Besides, the raw data can be in different types such as structured, semi-structured, or unstructured, etc. Thus, normalization, transformation, or collation can be useful to organize the data in a structured manner. In some cases, natural language processing techniques might be useful depending on data type and characteristics, e.g., textual contents. As both the quality and quantity of data decide the feasibility of solving the security problem, effectively pre-processing and management of data and their representation can play a significant role to build an effective security model for intelligent services.

Machine learning-based security modeling

This is the core step where insights and knowledge are extracted from data through the application of cybersecurity data science. In this section, we particularly focus on machine learning-based modeling as machine learning techniques can significantly change the cybersecurity landscape. The security features or attributes and their patterns in data are of high interest to be discovered and analyzed to extract security insights. To achieve the goal, a deeper understanding of data and machine learning-based analytical models utilizing a large number of cybersecurity data can be effective. Thus, various machine learning tasks can be involved in this model building layer according to the solution perspective. These are - security feature engineering that mainly responsible to transform raw security data into informative features that effectively represent the underlying security problem to the data-driven models. Thus, several data-processing tasks such as feature transformation and normalization, feature selection by taking into account a subset of available security features according to their correlations or importance in modeling, or feature generation and extraction by creating new brand principal components, may be involved in this module according to the security data characteristics. For instance, the chi-squared test, analysis of variance test, correlation coefficient analysis, feature importance, as well as discriminant and principal component analysis, or singular value decomposition, etc. can be used for analyzing the significance of the security features to perform the security feature engineering tasks [ 82 ].

Another significant module is security data clustering that uncovers hidden patterns and structures through huge volumes of security data, to identify where the new threats exist. It typically involves the grouping of security data with similar characteristics, which can be used to solve several cybersecurity problems such as detecting anomalies, policy violations, etc. Malicious behavior or anomaly detection module is typically responsible to identify a deviation to a known behavior, where clustering-based analysis and techniques can also be used to detect malicious behavior or anomaly detection. In the cybersecurity area, attack classification or prediction is treated as one of the most significant modules, which is responsible to build a prediction model to classify attacks or threats and to predict future for a particular security problem. To predict denial-of-service attack or a spam filter separating tasks from other messages, could be the relevant examples. Association learning or policy rule generation module can play a role to build an expert security system that comprises several IF-THEN rules that define attacks. Thus, in a problem of policy rule generation for rule-based access control system, association learning can be used as it discovers the associations or relationships among a set of available security features in a given security dataset. The popular machine learning algorithms in these categories are briefly discussed in “  Machine learning tasks in cybersecurity ” section. The module model selection or customization is responsible to choose whether it uses the existing machine learning model or needed to customize. Analyzing data and building models based on traditional machine learning or deep learning methods, could achieve acceptable results in certain cases in the domain of cybersecurity. However, in terms of effectiveness and efficiency or other performance measurements considering time complexity, generalization capacity, and most importantly the impact of the algorithm on the detection rate of a system, machine learning models are needed to customize for a specific security problem. Moreover, customizing the related techniques and data could improve the performance of the resultant security model and make it better applicable in a cybersecurity domain. The modules discussed above can work separately and combinedly depending on the target security problems.

Incremental learning and dynamism

In our framework, this layer is concerned with finalizing the resultant security model by incorporating additional intelligence according to the needs. This could be possible by further processing in several modules. For instance, the post-processing and improvement module in this layer could play a role to simplify the extracted knowledge according to the particular requirements by incorporating domain-specific knowledge. As the attack classification or prediction models based on machine learning techniques strongly rely on the training data, it can hardly be generalized to other datasets, which could be significant for some applications. To address such kind of limitations, this module is responsible to utilize the domain knowledge in the form of taxonomy or ontology to improve attack correlation in cybersecurity applications.

Another significant module recency mining and updating security model is responsible to keep the security model up-to-date for better performance by extracting the latest data-driven security patterns. The extracted knowledge discussed in the earlier layer is based on a static initial dataset considering the overall patterns in the datasets. However, such knowledge might not be guaranteed higher performance in several cases, because of incremental security data with recent patterns. In many cases, such incremental data may contain different patterns which could conflict with existing knowledge. Thus, the concept of RecencyMiner [ 170 ] on incremental security data and extracting new patterns can be more effective than the existing old patterns. The reason is that recent security patterns and rules are more likely to be significant than older ones for predicting cyber risks or attacks. Rather than processing the whole security data again, recency-based dynamic updating according to the new patterns would be more efficient in terms of processing and outcome. This could make the resultant cybersecurity model intelligent and dynamic. Finally, response planning and decision making module is responsible to make decisions based on the extracted insights and take necessary actions to prevent the system from the cyber-attacks to provide automated and intelligent services. The services might be different depending on particular requirements for a given security problem.

Overall, this framework is a generic description which potentially can be used to discover useful insights from security data, to build smart cybersecurity systems, to address complex security challenges, such as intrusion detection, access control management, detecting anomalies and fraud, or denial of service attacks, etc. in the area of cybersecurity data science.

Although several research efforts have been directed towards cybersecurity solutions, discussed in “ Background ” , “ Cybersecurity data science ”, and “ Machine learning tasks in cybersecurity ” sections in different directions, this paper presents a comprehensive view of cybersecurity data science. For this, we have conducted a literature review to understand cybersecurity data, various defense strategies including intrusion detection techniques, different types of machine learning techniques in cybersecurity tasks. Based on our discussion on existing work, several research issues related to security datasets, data quality problems, policy rule generation, learning methods, data protection, feature engineering, security alert generation, recency analysis etc. are identified that require further research attention in the domain of cybersecurity data science.

The scope of cybersecurity data science is broad. Several data-driven tasks such as intrusion detection and prevention, access control management, security policy generation, anomaly detection, spam filtering, fraud detection and prevention, various types of malware attack detection and defense strategies, etc. can be considered as the scope of cybersecurity data science. Such tasks based categorization could be helpful for security professionals including the researchers and practitioners who are interested in the domain-specific aspects of security systems [ 171 ]. The output of cybersecurity data science can be used in many application areas such as Internet of things (IoT) security [ 173 ], network security [ 174 ], cloud security [ 175 ], mobile and web applications [ 26 ], and other relevant cyber areas. Moreover, intelligent cybersecurity solutions are important for the banking industry, the healthcare sector, or the public sector, where data breaches typically occur [ 36 , 176 ]. Besides, the data-driven security solutions could also be effective in AI-based blockchain technology, where AI works with huge volumes of security event data to extract the useful insights using machine learning techniques, and block-chain as a trusted platform to store such data [ 177 ].

Although in this paper, we discuss cybersecurity data science focusing on examining raw security data to data-driven decision making for intelligent security solutions, it could also be related to big data analytics in terms of data processing and decision making. Big data deals with data sets that are too large or complex having characteristics of high data volume, velocity, and variety. Big data analytics mainly has two parts consisting of data management involving data storage, and analytics [ 178 ]. The analytics typically describe the process of analyzing such datasets to discover patterns, unknown correlations, rules, and other useful insights [ 179 ]. Thus, several advanced data analysis techniques such as AI, data mining, machine learning could play an important role in processing big data by converting big problems to small problems [ 180 ]. To do this, the potential strategies like parallelization, divide-and-conquer, incremental learning, sampling, granular computing, feature or instance selection, can be used to make better decisions, reducing costs, or enabling more efficient processing. In such cases, the concept of cybersecurity data science, particularly machine learning-based modeling could be helpful for process automation and decision making for intelligent security solutions. Moreover, researchers could consider modified algorithms or models for handing big data on parallel computing platforms like Hadoop, Storm, etc. [ 181 ].

Based on the concept of cybersecurity data science discussed in the paper, building a data-driven security model for a particular security problem and relevant empirical evaluation to measure the effectiveness and efficiency of the model, and to asses the usability in the real-world application domain could be a future work.

Motivated by the growing significance of cybersecurity and data science, and machine learning technologies, in this paper, we have discussed how cybersecurity data science applies to data-driven intelligent decision making in smart cybersecurity systems and services. We also have discussed how it can impact security data, both in terms of extracting insight of security incidents and the dataset itself. We aimed to work on cybersecurity data science by discussing the state of the art concerning security incidents data and corresponding security services. We also discussed how machine learning techniques can impact in the domain of cybersecurity, and examine the security challenges that remain. In terms of existing research, much focus has been provided on traditional security solutions, with less available work in machine learning technique based security systems. For each common technique, we have discussed relevant security research. The purpose of this article is to share an overview of the conceptualization, understanding, modeling, and thinking about cybersecurity data science.

We have further identified and discussed various key issues in security analysis to showcase the signpost of future research directions in the domain of cybersecurity data science. Based on the knowledge, we have also provided a generic multi-layered framework of cybersecurity data science model based on machine learning techniques, where the data is being gathered from diverse sources, and the analytics complement the latest data-driven patterns for providing intelligent security services. The framework consists of several main phases - security data collecting, data preparation, machine learning-based security modeling, and incremental learning and dynamism for smart cybersecurity systems and services. We specifically focused on extracting insights from security data, from setting a research design with particular attention to concepts for data-driven intelligent security solutions.

Overall, this paper aimed not only to discuss cybersecurity data science and relevant methods but also to discuss the applicability towards data-driven intelligent decision making in cybersecurity systems and services from machine learning perspectives. Our analysis and discussion can have several implications both for security researchers and practitioners. For researchers, we have highlighted several issues and directions for future research. Other areas for potential research include empirical evaluation of the suggested data-driven model, and comparative analysis with other security systems. For practitioners, the multi-layered machine learning-based model can be used as a reference in designing intelligent cybersecurity systems for organizations. We believe that our study on cybersecurity data science opens a promising path and can be used as a reference guide for both academia and industry for future research and applications in the area of cybersecurity.

Availability of data and materials

Not applicable.


Artificial Intelligence

Information and communication technology

Internet of Things

Distributed Denial of Service

Intrusion detection system

Intrusion prevention system

Host-based intrusion detection systems

Network Intrusion Detection Systems

Signature-based intrusion detection system

Anomaly-based intrusion detection system

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The authors would like to thank all the reviewers for their rigorous review and comments in several revision rounds. The reviews are detailed and helpful to improve and finalize the manuscript. The authors are highly grateful to them.

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A. S. M. Kayes, Paul Watters & Alex Ng

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This article provides not only a discussion on cybersecurity data science and relevant methods but also to discuss the applicability towards data-driven intelligent decision making in cybersecurity systems and services. All authors read and approved the final manuscript.

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Sarker, I.H., Kayes, A.S.M., Badsha, S. et al. Cybersecurity data science: an overview from machine learning perspective. J Big Data 7 , 41 (2020).

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  • Ransomware encryption: In this type, a return payment demanding message to decrypt the file’s encrypted symmetric keys will be displayed in return for the needed private asymmetric key.
  • Screen Lockers: This type of ransomware would display an official look-a-like message to prevent the users to use their device screen or logging in.
  • Scareware : this type of malware is motivated to manipulate the users to buy malicious software by creating nervousness on its used social engineering techniques.

There is a risk of cybersecurity in an organization/network/system when it lacks to update the relevant mechanisms, security procedures, and security measures . Hence, the best performance of security mechanisms to prevent unverified access to networks, devices, data, and programs . Yet there are some limitations for cybersecurity as listed below:

Limitations of cybersecurity

  • Fails in detecting the behavior of post attacks
  • Removal of delicate information to a remote location failure
  • Unsuccessful in detecting illegal sensitive data access
  • Lacking of detection in adversarial network access
  • Fails to process the finding of the resource by an attacker

While conducting research proposal in cybersecurity or being interested in being knowledgeable with the cyber security thesis ideas , you must know the above limitations in cybersecurity. In addition to the above lists, cybersecurity sets its layers on the basis of its performance, threat assessment, etc. So let’s have a look at the important cybersecurity layers.

What are the four important layers in cyber security?

  • Layer 4: Layer of Cyber Performance
  • Layer 3: Layer of Threat Assessment
  • Layer 2: Layer of Cyber Environment
  • Layer 1: Layer of Ecosystem

In the above, the layer of the ecosystem and the arrangement based on the layer of the working environment and the management layer is to develop and improve the set of cybersecurity tools . There are also models to implement cybersecurity research projects. For your better understanding, we classified its types as follows,

What are the types of cybersecurity Models?

  • Level of Application includes the requirement of changes in version of software, memory mapping, and source code.
  • Level of Network includes the network topology related information like traffic clouding and IP bouncing.
  • Level of Host includes the host asset changes in configuration changes like OS etc.

Thus the functional levels of the cybersecurity models are based on its host, network, applications that include supporting cybersecurity in various ways. There are some important cybersecurity models for performing the protection, threat identification and analysis, and network traffic usages as listed below.

Significant principles of Cyber Security Model

  • Apt classifying to differentiate threats from usual behavior
  • Significance of presenting effective situations
  • Necessity of real network traffic usage
  • Particularize and identify the relationships of identified attacks
  • Categorizing the detection ability based on logical threats
  • Detect sensor detectable tools that decomposed from logical threats

What are the steps of cyber security model?

  • To classify or forecasting the unknown information
  • Assessing and preparing the performance of the model
  • Select the Machine Learning algorithm, which is relevant
  • Selecting predictions based on feature engineering

The above steps and principles of the cybersecurity models are primarily based on the process of security, detection, responding, and recovering the data . Skilled models are also used in the cybersecurity field to identify the threats, choose the machine learning algorithm, and test the model’s performance. Apart from the uses of the model in cybersecurity, we provide you some important features of cyber threat detection.

Important Features of Cyber Threats Detection

  • Interval of packets sent, traffic flow ratio, protocol type, service type, bytes sent, average received size, flow direction, source/destination IP address, type of physical media, source/destination port addresses
  • Asset live report, type of requested DNS data, commanding answer, separate domain names, amount of queries on both the domain by name and time, IP addresses
  • Code stylometry, sandbox / AV proposals, PE file features, function length, CPU registers, Raised Exceptions, Windows registry, byte sequences, strings, opcodes APIs/System calls, availability of memory and file system

The function of cybersecurity doesn’t depend only on protecting the network. It also involves maintaining the proper functions of the cybersecurity models. Thus there’s a wide range of scope in the cybersecurity domain, as we mentioned earlier for cyber security thesis ideas . Here our research team has come up with some research domains of cybersecurity.

Research Areas of Cyber Security Thesis Ideas

  • Protecting the range of application: to sharing messages safely, this domain enables security structures in applications
  • Domain Security for Service Based Architecture (SBA): For interfaces based on the services, it compromises security structures for registering network essentials, detection, network and verification.
  • Protecting Configurability and Reflectivity: this includes the structures to notify about the active functions of security.
  • Protecting User Domain: it includes the security structures that allow the user to access user equipment
  • Protecting Availability of Network: the background of this domain ranges from helping the network to User Equipment (UE). It also involves the protection features that give safe and verified access for the users to network access.
  • Protecting Network Domain: to allow the system nodules to exchange user plane information security   and signaling this domain provides a combination of security structures.

The above-given research domains are the chief perspectives of cybersecurity. Not only in providing the cyber security thesis ideas , but we would also like to help you in all aspects of your thesis. For that, we give our entire support at any level of your project, and our world-class certified engineers would take care of your thesis with their innovative brains.

In the cyber security thesis alone, we have nearly 100 happy customers . Our technical team offers you practical explanations to make you understand your topic and the cyber security thesis . Thus you can overcome your thesis fear. We provide you on-time delivery service, so you can come to us at any level of your thesis completion. In this way, we are responsible for providing you the general subject viewpoint that should be mentioned in the thesis.

What described in the thesis chapters?

  • A general approach and outlining the category of the research, whether it is quantitative, experimental or qualitative.
  • Techniques used for data collection and proposed design i.e. methods (theory or statistical) used to examine the gathered information of the research.
  • Facts on the events of research like when, with whom or where does the research happening?
  • Resources and devices you used for research
  • Describing the difficulties you overwhelmed while conducting the research
  • Defending and assessing the research techniques
  • Declaring the appropriate research outcome with inferential and descriptive info precisely
  • Describe in a nutshell on the support of hypothesis and how the outcome is relevant to the research question
  • For the better understanding of the readers on your outcomes, you can include tables or figures.
  • Providing clear record on the outcomes that appropriates/inappropriate the research queries and explain the reason behind inappropriate outcomes.

Along with the thesis ideas that should be discussed inside the thesis chapters, a thesis generally has the power to decide your academic future. The effectiveness of every thesis is a debatable subject when you follow any profession related to your academic field. Our technical team’s practical explanation will help you understand the concept better to explain with others, and it is notable that we provide you confidential service

We assure you expected thesis results, and we are happy to extend our support and guidance for your research in other cyber security thesis ideas if you are interested. We provide you low-cost service, and we won’t let any chances for the cashback demands. We have years of reputation in project service, homework, and assignment writing.

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MILESTONE 1: Research Proposal

Finalize journal (indexing).

Before sit down to research proposal writing, we need to decide exact journals. For e.g. SCI, SCI-E, ISI, SCOPUS.

Research Subject Selection

As a doctoral student, subject selection is a big problem. has the team of world class experts who experience in assisting all subjects. When you decide to work in networking, we assign our experts in your specific area for assistance.

Research Topic Selection

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Literature Survey Writing

To ensure the novelty of research, we find research gaps in 50+ latest benchmark papers (IEEE, Springer, Elsevier, MDPI, Hindawi, etc.)

Case Study Writing

After literature survey, we get the main issue/problem that your research topic will aim to resolve and elegant writing support to identify relevance of the issue.

Problem Statement

Based on the research gaps finding and importance of your research, we conclude the appropriate and specific problem statement.

Writing Research Proposal

Writing a good research proposal has need of lot of time. We only span a few to cover all major aspects (reference papers collection, deficiency finding, drawing system architecture, highlights novelty)

MILESTONE 2: System Development

Fix implementation plan.

We prepare a clear project implementation plan that narrates your proposal in step-by step and it contains Software and OS specification. We recommend you very suitable tools/software that fit for your concept.

Tools/Plan Approval

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Pseudocode Description

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Develop Proposal Idea

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We perform the comparison between proposed and existing schemes in both quantitative and qualitative manner since it is most crucial part of any journal paper.

Graphs, Results, Analysis Table

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Project Deliverables

For every project order, we deliver the following: reference papers, source codes screenshots, project video, installation and running procedures.

MILESTONE 3: Paper Writing

Choosing right format.

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Collecting Reliable Resources

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Writing Rough Draft

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MILESTONE 4: Paper Publication

Finding apt journal.

We play crucial role in this step since this is very important for scholar’s future. Our experts will help you in choosing high Impact Factor (SJR) journals for publishing.

Lay Paper to Submit

We organize your paper for journal submission, which covers the preparation of Authors Biography, Cover Letter, Highlights of Novelty, and Suggested Reviewers.

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Get Accept & e-Proofing

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Publishing Paper

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MILESTONE 5: Thesis Writing

Identifying university format.

We pay special attention for your thesis writing and our 100+ thesis writers are proficient and clear in writing thesis for all university formats.

Gathering Adequate Resources

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Writing Thesis (Preliminary)

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Skimming & Reading

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Fixing Crosscutting Issues

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Organize Thesis Chapters

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Writing Thesis (Final Version)

We attention to details of importance of thesis contribution, well-illustrated literature review, sharp and broad results and discussion and relevant applications study.

How deal with significant issues ?

1. novel ideas.

Novelty is essential for a PhD degree. Our experts are bringing quality of being novel ideas in the particular research area. It can be only determined by after thorough literature search (state-of-the-art works published in IEEE, Springer, Elsevier, ACM, ScienceDirect, Inderscience, and so on). SCI and SCOPUS journals reviewers and editors will always demand “Novelty” for each publishing work. Our experts have in-depth knowledge in all major and sub-research fields to introduce New Methods and Ideas. MAKING NOVEL IDEAS IS THE ONLY WAY OF WINNING PHD.

2. Plagiarism-Free

To improve the quality and originality of works, we are strictly avoiding plagiarism since plagiarism is not allowed and acceptable for any type journals (SCI, SCI-E, or Scopus) in editorial and reviewer point of view. We have software named as “Anti-Plagiarism Software” that examines the similarity score for documents with good accuracy. We consist of various plagiarism tools like Viper, Turnitin, Students and scholars can get your work in Zero Tolerance to Plagiarism. DONT WORRY ABOUT PHD, WE WILL TAKE CARE OF EVERYTHING.

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Related Pages

University of Nebraska Omaha logo

MS in Cybersecurity Thesis

Target students.

The thesis option allows students to plan, execute, and report on an individual project that addresses a substantial problem covering both practical and scientific aspects. Through the thesis project, students demonstrate their understanding of and ability to apply the concepts, methods, and techniques covered in the MS in Cybersecurity core and elective courses. Students planning to pursue a PhD degree after completing the MS in Cybersecurity degree are particularly encouraged to pick the thesis option.

Thesis Scope and Content

The thesis must represent an independent effort to address a significant and well-scoped problem. The thesis should present a treatment of this problem that has both practical and academic merit. Although some thesis projects may have a more practical focus and others a more academic focus, both aspects should be covered.

The thesis must be much broader in scope than a one-semester class project. It should address a topic that supersedes the content of a single course. The thesis topic can be proposed by the student. Inspiration for possible topics comes from exposure to the materials covered in the core courses and the concentrations, from current articles in vocational and academic publications, and from ongoing research projects from graduate faculty.

Degree Requirements

33 semester hours

The student is responsible for finding a Graduate Faculty Member to approve the thesis topic and agrees to supervise the thesis. Next, the student prepares a thesis proposal.

While preparing the proposal, the thesis supervisor and student form a thesis committee. The committee must be composed of three Graduate Faculty Members. Non-graduate faculty members can participate as co-chair or member of the committee. Ex-officio members (e.g., representatives from the organization where the thesis project is executed) can be included on the committee. The composition of the committee has to be approved by the MS in Cybersecurity Graduate Program Committee and the Dean for Graduate Studies.

The student can enroll in thesis hours:

The 6 thesis hours are expected to be taken over two semesters. However, the student's thesis supervisor can petition the Graduate Program Committee to grant registration of all 6 hours in a single semester.

Thesis Defense

The student must defend the thesis during a public oral examination by the thesis committee in the last semester of enrollment. The thesis committee may decide to:

Graduate Program Requirements for International Students

International students should register for thesis along with other coursework, when possible. If the international student has not finished his/her thesis after two semesters (excluding summers), he/she will need to meet with his/her thesis supervisor and submit a projected date of completion form for an International Student Advisor's approval. It is recommended that the international student register for credit each semester in which he/she is working on the thesis.

UNO Graduate College - Proposed Supervisory Committee Form UNO Graduate College - Thesis Proposal Approval Form

Thesis Guide

UNO Graduate College - Guide for Preparing Theses

Cyber Threat Intelligence Architecture for Applied Cybersecurity Scenarios : PhD Thesis Proposal in Web Science and Technology

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60+ Latest Cyber Security Research Topics for 2023

Read it in 9 Mins

List of Trending Cyber Security Research Topics for 2023

Research area in cyber security, how to choose the best research topics in cyber security, the layout of cybersecurity research guidance, writing tips from expert, frequently asked questions (faqs).

Is by Doing

thesis on cybersecurity

The concept of cybersecurity refers to cracking the security mechanisms that break in dynamic environments. Implementing Cyber Security Project topics and cyber security thesis topics/ideas helps overcome attacks and take mitigation approaches to security risks and threats in real time. Undoubtedly, it focuses on events injected into the system, data, and the whole network to attack/disturb it.

The network can be attacked in various ways, including Distributed DoS, Knowledge Disruptions, Computer Viruses / Worms, and many more. Cyber-attacks are still rising, and more are waiting to harm their targeted systems and networks. Detecting Intrusions in cybersecurity has become challenging due to their Intelligence Performance. Therefore, it may negatively affect data integrity, privacy, availability, and security. 

This article aims to demonstrate the most current Cyber Security Topics for Projects and areas of research currently lacking. We will talk about cyber security research questions, cyber security research questions, cyber security topics for the project, best cyber security research topics, research titles about cyber security and web security research topics.

Cyber Security Research Topics

Digital technology has revolutionized how all businesses, large or small, work, and even governments manage their day-to-day activities, requiring organizations, corporations, and government agencies to utilize computerized systems. To protect data against online attacks or unauthorized access, cybersecurity is a priority. There are many Cyber Security Courses online where you can learn about these topics. With the rapid development of technology comes an equally rapid shift in cybersecurity trends, as data breaches, ransomware, and hacks become almost routine news items. In 2023, these will be the top cybersecurity trends. 

A) Exciting Mobile Cyber Security Research Paper Topics

B) Top Computer and Software Security Topics to Research

C) Trending Information Security Research Topics

D) Current Network Security Research Topics

E) Best Data Security Research Topics

F) Important Application Security Research Topics

G) Cybersecurity Law & Ethics Research Topics

H) Recent Cyberbullying Topics

I) Operational Security Topics

J) Cybercrime Topics for a Research Paper

The field of cyber security is extensive and constantly evolving. Its research covers a wide range of subjects, including: 

A good cybersecurity assignment heading is a skill that not everyone has, and unfortunately, not everyone has one. You might have your teacher provide you with the topics, or you might be asked to come up with your own. If you want more research topics, you can take references from Certified Ethical Hacker Certification, where you will get more hints on new topics. If you don't know where to start, here are some tips. Follow them to create compelling cybersecurity assignment topics. 

1. Brainstorm

In order to select the most appropriate heading for your cybersecurity assignment, you first need to brainstorm ideas. What specific matter do you wish to explore? In this case, come up with relevant topics about the subject, and select those relevant to your issue when you use our list of topics. You can also go to cyber security-oriented websites to get some ideas. Using any blog post on the internet can prove helpful if you intend to write a research paper on security threats in 2023. Creating a brainstorming list with all the keywords and cybersecurity concepts you wish to discuss is another great way to start. Once that's done, pick the topics you feel most comfortable handling. Keep in mind to stay away from common topics as much as possible. 

2. Understanding the Background

In order to write a cybersecurity assignment, you need to identify two or three research paper topics. Obtain the necessary resources and review them to gain background information on your heading. This will also allow you to learn new terminologies that can be used in your title to enhance it. 

3. Write a Single Topic

Make sure the subject of your cybersecurity research paper doesn't fall into either extreme. Make sure the title is neither too narrow nor too broad. Topics on either extreme will be challenging to research and write about. 

4. Be Flexible

There is no rule to say that the title you choose is permanent. It is perfectly okay to change your research paper topic along the way. For example, if you find another topic on this list to better suit your research paper, consider swapping it out. 

It is undeniable that usability is one of cybersecurity's most important social issues today. Increasingly, security features have become standard components of our digital environment, which pervade our lives and require both novices and experts to use them. Supported by confidentiality, integrity, and availability concerns, security features have become essential components of our digital environment.  

In order to make security features easily accessible to a wider population, these functions need to be highly usable. This is especially true in this context because poor usability typically translates into the inadequate application of cybersecurity tools and functionality, resulting in their limited effectiveness. 

Additionally, a well-planned action plan and a set of useful tools will help you write a high-quality research paper and remain motivated throughout the process. 

Studies in the literature have identified and recommended guidelines and recommendations for addressing security usability problems to provide highly usable security. The purpose of such papers is to consolidate existing design guidelines and define an initial core list that can be used for future reference in the field. 

The researcher takes advantage of the opportunity to provide an up-to-date analysis of cybersecurity usability issues and evaluation techniques applied so far. As a result of this research paper, researchers and practitioners interested in cybersecurity systems who value human and social design elements are likely to find it useful. You can find KnowledgeHut’s Cyber Security courses online and take maximum advantage of them.


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Businesses and individuals are changing how they handle cybersecurity as technology changes rapidly - from cloud-based services to new IoT devices. 

Ideally, you should have read many papers and know their structure, what information they contain, and so on if you want to write something of interest to others. 

The field of cyber security is extensive and constantly evolving. Its research covers various subjects, including Quantum & Space, Data Privacy, Criminology & Law, and AI & IoT Security. 

Inmates having the right to work, transportation of concealed weapons, rape and violence in prison, verdicts on plea agreements, rehab versus reform, and how reliable are eyewitnesses? 

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8 best cyber security books to read for 2023

Contributed by George Mack, Content Marketing Manager, Check Point Software.


Cyber security is an evolving field, where trends are always changing. As a result, modern cyber security tactics may lose their effectiveness rather rapidly. What worked last year does not as well today.

While cyber security tactics are continually updated and recycled, the list of books below focus on fundamental strategies that have withstood the test of time.

This list also contains books that adopt the format of a modern thriller, hijacking your attention as you witness what it’s like to be at the forefront of digital warfare or cyber espionage. Whether you’re a newbie or a veteran exec, reading the list of books below will deepen your understanding of cyber security and help you stay ahead of the pack.

Hacking: The Art of Exploitation

Hacking, the art of exploitation

Author: Jon Erickson

Hacking: The Art of Exploitation has been cited as “the most complete tutorial on hacking techniques,” explaining how a hacker thinks and then providing to the reader the step-by-step process of discovering new ways to solve problems related to computer systems and applications. The book walks readers through common techniques and tools, explaining the underlying logic behind dozens of different loopholes and attacks.

The second edition of the book come with a Live CD, which provides a complete Linux programming and debugging environment. You can use it to follow the book’s examples as you gain a deeper understanding of hacking techniques.

If you want to defend your organization against hackers, then you must learn how to think like one – and this book will help you achieve that.

“This book does a great job of covering C programming, assembly programming, vulnerability discovery, and exploitation all in one. If you are going to read only one book, start here.” —Dino Dai Zovi

The Art of Software Security Assessment

The art of software security assessment

Authors: Mark Dowd, John McDonald, Justin Schuh

This is known as the “Bible” of OS-level exploitation. The Art of Software Security Assessment explains how to audit security in applications of all sizes and functions. In addition, you will find real examples of past code that were found in high-profile applications. This book covers a wide array of topics, including: code auditing, UNIX/Linux assessment, Windows-specific issues such as objects and the filesystem, IP stacks, firewalls, common application protocols, threat modeling, and more.

If you’re a beginner, this shouldn’t be your first book, as it requires proficiency with programming. However, if you’re looking to do more with software security audits, then this is a must-read.

“ There are a number of secure programming books on the market, but none that go as deep as this one. The depth and detail exceeds all books that I know about by an order of magnitude. ” — Halvar Flake, CEO and head of research, SABRE Security GmbH

The Web Application Hacker’s Handbook

The web application hacker's handbook

Author: Dafydd Stuttard

Web applications are a common attack vector into most companies, making them vulnerable to threats that steal sensitive data and compromise customer accounts. This book explores various techniques for attacking and defending the growing range of web applications. You will learn about frame busting, hybrid file attacks, cross-domain integration techniques, HTTP parameter pollution, and more.

An advantage of this book is that it breaks down the information into a straight line, building upon your understanding of a new topic from the previous one. If you’re interested in discovering and preventing web application flaws, then this book is for you.

“This is right up there with Homer’s Odyssey, Shakespeare’s Romeo and Juliet and quite frankly, The Bible. Ok, maybe that’s pushing it but you get the idea.”

Practical Malware Analysis

Practical malware analysis

Author: Michael Sikorski

It seems that malware has always been a plague in the modern world of computers. Hackers are always looking for new ways to program malware that bypass traditional detection methods, keeping security professionals on their toes.

This is a very comprehensive book for all things malware-related. Although the topic of dissecting malware can be daunting, as it requires the ability to interpret code and understanding how internal systems work, this book does an excellent job of relaying those concepts. You will also learn how to overcome malware tricks that hackers often use such as obfuscation, anti-debugging, anti-disassembly, and anti-virtual machine techniques.

However, this book requires an understanding of Assembly and the x86 architecture, as advanced analysis happens at the assembly level.

“. . . the most comprehensive guide to analysis of malware, offering detailed coverage of all the essential skills required to understand the specific challenges presented by modern malware.”

― Chris Eagle, Senior Lecturer of Computer Science at the Naval Postgraduate School

Social Engineering: The Science of Human Hacking

Social engineering: The science of human hacking

Author: Christopher Hadnagy

Humans are the weakest link. This is a phrase that we often hear. It highlights the fact that human errors, not technical ones, are often the cause of a major cyber incident or breach. Phishing attacks successfully exploit this flaw: according to Verizon’s DBIR, 22% of data breaches involve phishing .

In Social Engineering , Christopher Hadnagy explains the steps that social engineers use to deceive you and ultimately extract information out of you. While the book does provide tools you can use, technology is constantly changing, so you have to keep in mind that what worked in the past may not work today.

However, human behavior doesn’t change. By understanding the fundamentals of “human hacking” techniques, you will be better equipped to defend your organization against these attacks.

“The result is a remarkably good read that is both informative and enjoyable. If you are security professional or simply concerned about your own security, then you really should read this book.”

The Art of Invisibility

The art of invisibility

Author: Kevin Mitnick

This book was written by Kevin Mitnick, dubbed “The World’s Most Famous Hacker.” The book’s thesis is that we are all being tracked 24/7. Whenever you use your e-mail, visit a website, call on your cell-phone, or use WiFi, you are most likely being tracked. Does your debit card have a little silver-colored, square chip on it? Have you ever wondered how much personal information it contains about you? Kevin discusses all these topics.

However, after learning all you can about privacy (or your lack thereof), he doesn’t just leave you in a state of paranoia. Kevin provides you with best practices and techniques to go “invisible,” preventing others from tracking you.

“The world’s most famous computer hacker and cybercult hero…has written a blueprint for system security based on his own experiences. Required reading for IT professionals, this book is highly recommended for public, academic, and corporate libraries.”  ― Library Journal

The Cuckoo’s Egg

The cuckoo's egg

Author: Cliff Stoll

The vulnerabilities of a network aren’t always in the places you think they are. This is the real life story of how the author discovered what appeared to be a 75 cent accounting error that eventually revealed the presence of an unauthorized user on his system. The hacker’s code name was “Hunter,” a mysterious threat actor who infiltrated into US computer systems and extracted sensitive military and security data. The author began spying on the spy, resulting in a game of deception, satellites, and missile bases. Although this book is about an incident that occurred decades ago, it highlights the one characteristic that any security professional must have for a successful career.

“Clifford Stoll’s first book, ”The Cuckoo’s Egg,” is both a gripping spy thriller and an intriguing introduction to the futuristic world of international computer networking. It presents a rare view from inside the global village that has been created by the new technologies of data communication. Most improbable of all, this is a true story, subverting our expectations in ways too surprising to be fictitious.”

― Jed Harris

Countdown to Zero Day

Countdown to zero day

Author: Kim Zetter

Countdown to Zero Day takes a deep dive into the release of Stuxnet, the world’s first digital weapon, and how it impacted an Iranian nuclear facility. Not only will you learn more about Stuxnet, but you will also gain a deeper understanding of cyber warfare in general.  This is a must-read book that provides insight into the intersection of infrastructure and malware, and how high the stakes have reached in the modern world.

“Author Kim Zetter’s malware record in Countdown to Zero Day is incredible. It is a profoundly fascinating, innovative investigative story.”

We hope you enjoyed our list! These are the best cyber security books you can read as we enter 2023. This combination of books should improve not only your knowledge of the cyber world, but will also entertain you with thrilling stories of cyber espionage and warfare.

Further reading: Can iPhones get viruses from websites?


10 highly effective ways to improve your risk management methodology

10 highly effective ways to improve your risk management methodology

Vivek Gullapalli, Field CISO, APAC

Key strategies for CISOs in a dynamic threat landscape

Pete Nicoletti, Field CISO Americas

How hackers bypass Zero Trust and what you can do about it

Subscribe to Weekly Digest for the most current news and insights.

thesis on cybersecurity

PHD Guidance

From this article, you can gain the latest Master Thesis Topics in Cybersecurity with their research areas and challenges!!! Cybersecurity is the technology to protect electronic devices and records by defensive measures against the security threats and vulnerabilities of the system. By the by, the data in the communication network are shared between the one-to-one / one-to-many users. Here, it uses relay nodes for passing data over either wired or wireless transmission channels.

Majorly, these channels are affected by the noise along with the combo of 3 primary malicious violations . And, they are injection attacks (inject false data), eavesdropping attacks (overhear the data), and DoS attacks (make network shutdown) . For more clarity, this article mainly focuses on the below-specified research questions which figure out the Kolmogorov Complexity .

Now, we can see the outline of the cyber-attacks through their threats, vulnerabilities, cyber risk, and threat sources . This summary gives you updated information about Cybersecurity in the current digital world.

Master Thesis Topics in Cybersecurity  Research Guidance

Overview of Cyber Threats / Attacks

A cyberattack is an aggressive operation of malicious attackers to get the accessibility of the network infrastructure, connected external hardware, and system data . Here, this attacker will act as both an unlicensed user and software which relies on malicious activities. On implementing these attacks over the system, the private information may get modified, lost, modified, and deleted without the knowledge of respective legal users . In general, the attacks are commonly categorized as passive and active types, which are mentioned below,

Further, these attacks in Cybersecurity can be performed either internally or externally, which is termed as inside attack and outside attack.  Let’s see them in detail in the below points,

By knowing the significance of Cybersecurity, our research team will regularly modernize their knowledge in current emerging cyber-security attacks . As a result, we are familiar with all recent intelligent techniques for detecting and preventing cyber-attacks, threats, and vulnerabilities . Below, we have given you fundamental attacks which play a significant role in framing current Master Thesis Topics in Cybersecurity .

Types of Cyber-Attacks

From the above attacks, here we have taken the phishing attacks as an example. This will make it clear how the phishing attack is controlled in the organization . For this purpose, it uses a multi-layered approach to identify and mitigate the phishing attack, which is given below,

Next, our research team has shared facts on how the effect of cyber-attackers is analyzed in cybersecurity studies . For this purpose, it uses two primary vital criteria to evaluate cyber-attackers. So, this helps know the risks created by attackers in the cyber system.

How to assess the impact of cyber-attackers?

Our team of experts in the cybersecurity research field is passionately working on its current research areas to develop effective techniques for preventing and defending web-based attacks. Because of this regular habit of technical updates, we have found numerous research ideas for defeating a different variety of attacks such as social engineering attacks, zero-detection, ransomware attacks, and many more. Now, let’s see the diverse techniques that used in recent Cybersecurity.

Cybersecurity Approaches

At the present time, Cybersecurity is growing in all the digital information communication areas . So, it is seeking for the fine-tuned risk prevention techniques to detect, analyze and appraise the system performance in terms of security and privacy . Most importantly, these techniques are manipulated by both real environ and cyber systems. Further, it is categorized with the individual goal to achieve maximum security .

In addition, the cyber system works on some self-protective measures over the cyber-attacks . These measures are based on the aspects of attack detection, attack mitigation, and attack prevention . Besides, these solutions/protocols are needed to meet the following security properties. Overall, these measures promise to give security over any behavior of adversaries in the following aspects.

So far, we have debated on cyber threats, attacks, attack assessment factors, various security approaches . Now, we can see the different research perspectives of the cybersecurity field. For that, our developers have listed out few recent Master Thesis Topics in Cybersecurity . More than this, we have a massive number of novel ideas for active scholars.

Master Thesis Topics in Cybersecurity

Impact Analysis of COVID-19 on Cybersecurity

In accordance with COVID-19, there are several attacks that threaten system security and privacy. The commonly found attacks are Malspams, ransomware attacks, and phishing attacks .

Herein, these attacks are performed by the cyber-criminals to access the COVID 19 vaccine-related study and progress information which affects the government privacy information. So, it has the Threat of nation-information insecurity. Though it takes many preventive measures, it is necessary to concern about a person’s private data. For that, it requires reconsidering the existing privacy policies for enhanced security technologies. 

Further, it also needs to be pre-emptively signifying the security solutions of the post-COVID-19, which creates a revolutionary impact on pre-COVID-19. By the way, it also emphasizes secrecy mitigation, risk evaluation, and threats of post-COVID-19 . On the whole, in the increasing cyber-crime environment, we need to develop intelligent techniques of Cybersecurity. Now, let’s see the effect and challenges of COVID-19 in cybersecurity systems,

Different Cyber Security Issues

Cybersecurity Master Thesis Research Guidance

Master Thesis in Cybersecurity Research Guidance

Our Master Thesis Topics in Cybersecurity helps you to develop any kind of challenging applications. In fact, we have a team of experts in technical research, development, and writing teams. These experts assist you in showcasing the research conducting competencies and intellectual writing skills to the world.

For this purpose, we help you to write a fine-tuned thesis in different chapters such as introduction, literature survey, methodologies, discussion of result, and conclusion . It may also vary based on the institution’s requirements. Here, we are unique in denoting the way we gather data, examine data, and more. On the whole, the thesis plays a significant role in presenting the experimental research results cybersecurity dissertation . 

As mentioned earlier, the above-specified thesis chapters are common in all quantitative and qualitative research . And, based on the design of this research type, the way of conducting, studying, and discussing research will vary.

On the whole, you can avail yourself of our research service in any phase of your research. Once you hold your hand with us, we will take full responsibility for your Master Thesis in Cybersecurity to yield the best research outcome.

thesis on cybersecurity

Cyber Security Essays

Advanced cyber security and its methodologies.

Digital Civilization has turned into a critical wellspring of data sharing and proficient exercises like business, saving money exchanges, shopping, and administrations and With the expansion in utilization of the internet, cybercriminal exercises are additionally expanding exponentially. The fundamental reasons is that with the commencement of internet, the web applications were likewise getting prevalence for information putting away and information sharing, regardless of the client. With the progression of time, web applications were getting more intricate with quick increment in […]

Cyber Security for our Generation

Some of the biggest threats to our national security often go unnoticed. These threats often are not publicized, and no emphasis is given to them. These are some of the biggest threats that people face in our generation. The shocking part about these statistics is that they are often covered up, or at least they are attempted to be. One of the biggest storylines that has happened in 2018 so far has been the Facebook data scandal. Now while this […]

Reasons of Cyber Attacks

1. Substandard User ID and Password Every individual need to have their own password secure and stronger. For an instance strong password can be obtained by latest maintaining minimum of having15-character length with an least one special character, number, capital and small alphabet. Most importantly choosing password like own name, date of birth, phone number may become hacker to simply figure out easy to break through your personal account security. In the same way User ID should not be shared […]

Cyber Security and how to Prevent Cyber Crime

Cybercrimes are interrupting normal computer functions and has brought many known companies and personal entities to their knees. Over the last decade, crime has entered into the world of information. Crime is developing gradually since the days when merchandise was transported by stagecoach, theft or extortion has changed to keep up, even to our modern-day equivalent-credit and debit cards. Stealing credit card number has become well known danger. In the present, internet has become a playing field for computer attackers. […]

Cyber Security Threats on the State Level

This paper examines two notable events of cyber warfare and security in our current age (the Stuxnet attack on centrifuges, and the Petya ransomware affecting citizens and governmental agencies), as well as examines how these attacks shape foreign and domestic policies and procedures. By examining the extent of the damage of these two attacks, I will argue that cyber warfare events will not just affect governmental systems, but would ultimately cause destruction to the layman’s infrastructure, further crippling any state […]

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How Pervasive is the Internet in your Life?

Q.1 How pervasive is the internet in your life? How much do you think society has come to depend on the Internet? Answer: Our writers can help you with any type of essay. For any subject Get your price How it works Need a custom essay on the same topic? Give us your paper requirements, choose a writer and we’ll deliver the highest-quality essay! Order now When it comes to how pervasive the internet is in current life, my answer […]

Substations: Smart Grid & Cyber Security Threats

Transferring from old energy network to a new technology such as smart grids. It changes the energy industry worldwide to better quality, manageability and performance. It gives us the ability to operate it by communications, monitor and control it. However, using communications in smart grid increase connectivity causing our security to be exposed and make it more challenge to protected. It can be a target for hackers, and cyber terrorism. Thus, it got governments, consumer and industry attention to increase […]

Cyber Security Threats in Healthcare

Cyberattacks have been targeting the healthcare industry, among the biggest industries in the US, in the 2018 period. The implication is that it has come time to improve the protection of institutional and patient information with a more tailored approach to this threat. In comparison with other industries, many health organizations have engaged in inadequate investment in cybersecurity while spending approximately as much money as other industries. It is quite worrying when phishing cyberattacks, as well as breaches of patient […]

Constant the Rise of Technologies and Cyber Threats

There is a wide range of cyber threats that happen every day, it is important that we follow all of the necessary precaution’s in order to ensure the safety of our private information including but not limited to passwords, network credentials, banking or credit card information. Malicious attacks occur more frequently than one would expect, their purpose is to damage a device. Most of us are unaware of the weaknesses we have within our smartphone’s security settings. With that being […]

Health Care Cyber Security

Healthcare is an industry section that has turned out to be unstable and basic in this expanding computerized view. This requires an association’s data security program to be legitimately organized as there is no edge for the blunder, which could without much of a stretch mean an actual existence and-passing situation. This article advances both essential specialized and business worries that regularly get away from the medicinal services data security program radar. On the specialized side, broad multiplication of information […]

Essay of Cyber Security Education

The experts and professionals of matters related to cyber security should assign the participant puzzles whereby they should divide themselves into various teams as indicated in the framework of NICE, and each group should specialize in a specific area. There is a wide range of ideas on the cyber security where the riddles may come from the fields like Wireshark, protection of website application, analysis of digital systems, and social engineering. There should be a task force created to conduct […]

Impact of Technology on Privacy

The 21st Century is characterized by the heavy impact technology has on us as a society while it continues to develop new devices and modernize technology. Millions of individuals around the world are now connected digitally, in other words, people globally rely heavily on smartphones tablets, and/ or computers that store or save a majority of their personal information. Critical and extremely personal data is available and collected in these smart technology such as credit card details, fingerprint layout, and […]

Cyber Security for the Average American

According to statistics, the average American spends 10 hours per day using technology. Whether it be a cellphone, tablet or laptop, that’s more than 40 hours a week online. We think that we’re safe, but part of living in this 21st century is understanding that our so-called private information can easily accessed by the wrong person and made public. I am sure you have heard, at some point, news pertaining to identity theft or data breaches, with the effects being […]

The E-Commerce and Cyber Security

The wish is the online e-commerce company that will provide the opportunity for all shoppers to find their favourite wordrobe online in all of the world. Their wardrobe could be included dresses, skirts, jeans and etc…. This company was founded in 2010 and also have the App for their over 100 million users on the iOS and android platform. The E-Commerce servers for this company is located in four cites internationally, two are in the USA, the headquarter in Alexandria […]

About Cybersecurity

I believe everything that is created by man can also be destroyed by it. Humans have proved to be the most intelligent species in this world. We have created the technology that appears to be smarter than the human brain but if it overpowers the human intelligence it can be destroyed as well. Internet works in the same manner. It has created dependencies that have led to millions of people relying on this technology in getting every task done no […]

Defining Cybersecurity Law

INTRODUCTION In “Defining Cybersecurity Law”, by Jeff Kosseff, the author appears to be more concerned with improving cybersecurity law than he is with defining it. In this paper, I will give a brief summary and critique of the four substantive sections of this article. I will end with a brief mention of aspects of cybersecurity law that the author missed. My main issues with this article are the author’s (1) preoccupation with prevention of cybersecurity breaches instead of balancing security […]

Laws of Cybercrimes

Abstract This paper examines the cyber security and its challenges in current temperamental circumstance of security in present world. These day’s innovation of technology persistently developing more quickly than expected. As a public that runs on latest innovation technologies, we are likewise therefore reliant on it. Where similarly as innovation of technology brings ever more noteworthy advantages, it likewise brings ever more prominent threats. We should look some significant concerns confronting that incorporate threats, information theft, identity theft, cyber war, […]

Cybersecurity Today

Network (internets) are not secure enough due to the lack of efficient cybersecurity. As a result, ransomware attacks are increasing affecting most businesses and individuals today. Enacting measures to detect cyberattacks and ransomware attacks can be helpful in preventing unforeseen repercussions from the attacker in the corporate network. Cybersecurity needs to implement new policies and recommendations so that the ransomware attack can reduce. This report will first discuss some ransomware attack that as happened before; next the report will discuss […]

Cybersecurity as a Form of Digital Protection

Cybersecurity is an ever-growing form of digital protection created and used for the sole purpose of protecting confidential information against hard drive malfunctions, power outages, and adversaries. In Healthcare, it is crucial for hospitals and health providers to keep up with the security of digital health data through cybersecurity in order to comply with The Health Insurance Portability and Accountability Act (HIPAA) and avoid potentially devastating consequences. Insider threats, access control breaches, and network breaches are some of the main […]

Cybersecurity Paper

With cybersecurity attacks on the rise, the ability of an organization to insure uninterrupted operations is an imperative. No longer can an organization solely rely upon software applications to identify and mitigate cyber risks. It takes a skilled team lead by an experienced manager to holistically address an organizations technology risks. The National Infrastructure Advisory Council’s (NIAC) definition of infrastructure resilience is “the ability to reduce the magnitude and/or duration of disruptive events. The effectiveness of a resilient infrastructure or […]

Developing and Testing Photorealistic Avatar with Body Motions and Facial Expressions for Communication in Social Virtual Reality Applications

Developing and Testing Photorealistic Avatar with Body Motions and Facial Expressions for Communication in Social Virtual Reality Applications Abstract Our writers can help you with any type of essay. For any subject Get your price How it works Need a custom essay on the same topic? Give us your paper requirements, choose a writer and we’ll deliver the highest-quality essay! Order now Providing effective communication in social virtual reality (VR) applications requires a high realism of avatar representation and body […]

Virtual Reality: Game Transfer Phenomena

Imagine if you were you were floating through space, watching a horror film,s or perhaps playing a video game, and it seemed like you were actually there. With the invention of virtual reality (VR), people are able to explore the illusion of this reality. Virtual reality is computer-generated technology used to create a manufactured environment. There is a range of systems that are used for this purpose such as special headsets and fiber optic gloves. The term virtual reality means […]

The Real Issue Behind Cyber-Security

The steady trend towards digitalization has been occurring for a long time, and as of lately, a new type of crime market has risen alongside digitalization. In recent years, companies all over the world have been affected by some form of cybersecurity issue whether that be attacks to infrastructure or momentary paralyzation of the company itself through the exploitation of security measures. Over the years the number of attacks all around the world has increased exponentially with many more cyber-attacks […]

Cybersecurity for a Successful Acquisition Report

The act of conducting a policy gap analysis is crucial in determining any missing overlap or technical deficiencies when planning to join the IT architecture and network topologies of two or more companies. During the acquisition process, the policies of either party will be examined in order to confirm current software updates and patches, proper configuration of tools, and employee protocol during the transition. Once the initial merger is complete, it’ll be important to compare the outcome with each company’s […]

Cybercrimes: an Unprecedented Threat to the Society

What is a Cybercrime? Cybercrime, or computer-oriented crime, is the crime that involves computer and its network. The computer may have been used in the commission of a crime, or it may be the target. Cybercrimes can be defined as: “Offences that are committed against individuals or groups of individuals with a criminal motive to intentionally harm the reputation of the victim or cause physical or mental harm, or loss, to the victim directly or indirectly, using modern telecommunication networks […]

Cybersecurity Issues in Societal Perspective

E-governance and Cybersecurity Documents issued by a country’s government provides a personal identity to an individual. Driver’s licenses, social security numbers, tax identification numbers, and various other entitlement documents are used on a regular basis by people to demonstrate their identity and authorization for various opportunities. Because these documents form the basis for all subsequent documents, their integrity is of high importance to stakeholders. Therefore, these crucial documents are targets for criminals and further cyberattacks (Conklin, A., & White, G. […]

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Related topic

Essay About Cyber Security Cybersecurity is defined as “the practice of defending computers, servers, mobile devices, electronic systems, networks, and data from malicious attacks” (What is Cyber Security). The main categories of cybersecurity include network security, application security, information security, and operational security. Network security involves securing a network from intruders such as targeted attackers or malware. Application security includes keeping devices and software free of threats. Information security protects data in both storage and transit. Operational security is the decisions and processes used when handling and protecting data and networks. However, the most important aspect of cyber security involves end-user education because people are the most unpredictable cyber-security factor. Cyber Threats According to RiskBased Security, “within the first nine months of 2019, there have been 5,183 breaches reported with 7.9 billion records exposed” (RBS). The vast majority of these cybersecurity breaches are the responsibility of malicious criminals and have been experienced by medical services, public entities, and retailers. According to the International Data Corporation, ‘worldwide spending on security-related hardware, software, and services is forecast to reach $133.7 billion in 2022” (New IDC Spending Guide). There are three types of cyber threats: cybercrime, cyber-attacks, and cyber-terrorism. Cybercrime is defined as single assailants or groups that target systems for financial gain or to cause disruptions. Cyber-attacks are usually politically motivated and involve information gathering. Cyber terrorism‘s goal is to cause mass panic or fear through undermined electronic systems. The most common cyber threat is malware, or “software that a cybercriminal or hacker has created to disrupt or damage a legitimate user’s computer” (What is Cyber Security). Malware can take on many different forms and can be easily spread through seemingly legitimate email attachments or supposedly harmless downloads. One form of malware is a virus or self-replicating program that spreads by infecting computer files with malicious code. A Trojan is malware disguised as legitimate software that, once downloaded, collects data or inflicts damage. Spyware is a form of malware that records information on a user, such as credit card or bank details. Ransomware blocks a user from accessing important files or data unless a ransom is paid to the attacker. Adware utilizes advertising software to spread malicious attacks. Lastly, botnets are networks of computers that have been infected with malware and are used online without user permission. End-User Protection End-user protection, also called endpoint security, is a crucial aspect of cybersecurity because “it is often an individual (the end-user) who accidentally uploads malware or another form of the cyber threat to their desktop, laptop or mobile device” (What is Cyber Security). The first cybersecurity measure that can be taken to protect end-users is encryption. Cryptographic protocols are used to encrypt emails, files, and critical data and guard against loss, theft, and tampering. Additionally, end-user security software can be used to scan for malicious code, quarantine the threat, and then completely remove it from the computer. By focusing on real-time malware detection, electronic security protocols can “use heuristic and behavioral analysis to monitor the behavior of a program and its code to defend against viruses or Trojans that change their shape with each execution” (What is Cyber Security). These security programs are able to learn from and analyze malware in order to understand how to better detect new threats. 

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  1. (PDF) Cyber Security for Our Digital Life

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  1. Cybersecurity Career: How to Make a Career in Cybersecurity 2022

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  3. An introduction to digital assets and Web3 as an investment theme

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  6. This CEO puts his number on his national commercial cybersecurity company



    My hypothesis is that we need a national shift in thinking on cybersecurity; we need to strengthen the cyber culture. To that end, I will look at societal changes using the examples of two issues that are independent of each other, smoking and seat belt use.

  2. Exploring Industry Cybersecurity Strategy in Protecting Critical

    The purpose of this qualitative multiple case study was to explore cybersecurity strategies used by information technology (IT) managers and compliance officers to mitigate cyber threats to critical infrastructure.

  3. PDF Managing Cybersecurity As a Business Risk for Small and Medium

    1 Introduction The topic of cybersecurity or rather insecurity in cyberspace has been a popular topic in the media and on Capitol Hill. Most of the attention has focused on high-profile data breaches and government mandates with little attention to possible solutions for mitigating such issues.

  4. Good cybersecurity thesis topics for a master's degree

    Good cybersecurity thesis topics for a master's degree Writing a master's thesis? A strong topic positions you for academic and professional success, while a weak one promises to make an already intensive process arduous at best. By Alissa Irei, Senior Site Editor


    THREE ESSAYS ON CYBERSECURITY-RELATED ISSUES By HE LI A dissertation submitted to the Graduate School- Newark Rutgers, The State University of New Jersey in partial fulfillment of requirements for the degree of Doctor of Philosophy Graduate Program in Management Written under the direction of Dr. Miklos A. Vasarhelyi and approved by

  6. (PDF) Thesis on Cyber Security Management and PolIcy

    Thesis on Cyber Security Management and PolIcy May 2021 Authors: Ojo Ademola American International University West Africa Preprints and early-stage research may not have been peer reviewed yet....

  7. A Systematic Literature Review on the Cyber Security

    Cyber security is a set of technologies, processes, and practices aimed at preventing attacks, damage, and illegal access to networks, computers, programmes, and data.

  8. MSc thesis

    MSc thesis MSc thesis on Cyber Security related topics defended at the University of Tartu : 2019: Dissanayake. P.G., "A Comparison of Security Risk Analysis in the In-house IT Infrastructure and Cloud Infrastructure for the Payment Gateway System" Mammadzada. K., "Blockchain Oracles" Matsalu.

  9. 50 Cybersecurity Research Paper Topics

    50 Great Cybersecurity Research Paper Topics Students are required to write papers and essays on cyber security topics when pursuing programs in cyber security disciplines. These topics are technical and they require learners to inherently understand this subject. What's more, students should have impeccable research and writing skills.

  10. PDF [email protected] State University

    sectors such as healthcare, finance, transportation, and cybersecurity. Thus, researchers in the cybersecurity field are increasingly using machine learning to detect, prevent and re-spond to cyber threats. This chapter will categorize previous works that focus on detecting ransomware attacks based on the types of analysis mentioned in 1.3.

  11. Cybersecurity data science: an overview from machine learning

    In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data ...

  12. Trending Top 15 Cybersecurity Thesis Topics

    The intention to develop cybersecurity thesis topics is to ensure the safety, privacy, trust, and integrity of the cyberinfrastructure. For this purpose, it determinedly fights against malicious cyber-attacks, threats, and vulnerabilities.

  13. PDF 2010:050 MASTER'S THESIS Internal threat to information security

    This thesis is based on theoretical review and one case study. The research question in this thesis is: How information security countermeasures are perceived by employees and how they change their behavior affecting internal threat level.


    Top 9 Cyber Security Thesis Ideas [PhD & MS Scholars] Novel Proposal TRENDING 9 CYBER SECURITY THESIS IDEAS Cybersecurity itself contains the sense of securing the network, files, or programs from unverified, illegal entry of external and internal individuals. While operating the virtual environment, it is important to ensure the privacy of data.

  15. MS in Cybersecurity Thesis

    The thesis option allows students to plan, execute, and report on an individual project that addresses a substantial problem covering both practical and scientific aspects. Through the thesis project, students demonstrate their understanding of and ability to apply the concepts, methods, and techniques covered in the MS in Cybersecurity core ...

  16. Cyber Threat Intelligence Architecture for Applied Cybersecurity

    This paper presents the proposed work for a PhD thesis in Web Science and Technology, scheduled for completion in July 2023. This doctoral thesis falls within the area of Computer Engineering, with applicability in the domain of Cybersecurity and consequently in the subdomain of Threat Intelligence.

  17. A comprehensive review study of cyber-attacks and cyber security

    In addition, five scenarios can be considered for cyber warfare: (1) Government-sponsored cyber espionage to gather information to plan future cyber-attacks, (2) a cyber-attack aimed at laying the groundwork for any unrest and popular uprising, (3) Cyber-attack aimed at disabling equipment and facilitating physical aggression, (4) Cyber-attack as a complement to physical aggression, and (5 ...

  18. 60+ Latest Cyber Security Research Topics for 2023

    Implementing Cyber Security Project topics and cyber security thesis topics/ideas helps overcome attacks and take mitigation approaches to security risks and threats in real time. Undoubtedly, it focuses on events injected into the system, data, and the whole network to attack/disturb it.

  19. Cybersecurity Essays: Examples, Topics, Titles, & Outlines

    Paper #: 86099763 Read Full Paper Cyber Security Conceptual or Substantive Assumptions Cybersecurity is fast approaching a place where it is becoming a form of currency with consumers and clients. It is only in the past decade that digital environments became a normal and typical way to transact business.

  20. 8 best cyber security books to read for 2023

    The Art of Invisibility. Author: Kevin Mitnick. This book was written by Kevin Mitnick, dubbed "The World's Most Famous Hacker.". The book's thesis is that we are all being tracked 24/7. Whenever you use your e-mail, visit a website, call on your cell-phone, or use WiFi, you are most likely being tracked.

  21. Master Thesis in Cybersecurity Research Guidance

    1 2 3 4 From this article, you can gain the latest Master Thesis Topics in Cybersecurity with their research areas and challenges!!! Cybersecurity is the technology to protect electronic devices and records by defensive measures against the security threats and vulnerabilities of the system.

  22. Cyber Security Essays

    26 essay samples found Essay examples Essay topics Advanced Cyber Security and its Methodologies Words: 1105 Pages: 4 4544