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Nilă, Constantin, Patriciu, Victor.  2020.  Taking advantage of unsupervised learning in incident response. 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). :1–6.
This paper looks at new ways to improve the necessary time for incident response triage operations. By employing unsupervised K-means, enhanced by both manual and automated feature extraction techniques, the incident response team can quickly and decisively extrapolate malicious web requests that concluded to the investigated exploitation. More precisely, we evaluated the benefits of different visualization enhancing methods that can improve feature selection and other dimensionality reduction techniques. Furthermore, early tests of the gross framework have shown that the necessary time for triage is diminished, more so if a hybrid multi-model is employed. Our case study revolved around the need for unsupervised classification of unknown web access logs. However, the demonstrated principals may be considered for other applications of machine learning in the cybersecurity domain.
Qian, Kai, Dan Lo, Chia-Tien, Guo, Minzhe, Bhattacharya, Prabir, Yang, Li.  2012.  Mobile security labware with smart devices for cybersecurity education. IEEE 2nd Integrated STEM Education Conference. :1—3.

Smart mobile devices such as smartphones and tablets have become an integral part of our society. However, it also becomes a prime target for attackers with malicious intents. There have been a number of efforts on developing innovative courseware to promote cybersecurity education and to improve student learning; however, hands-on labs are not well developed for smart mobile devices and for mobile security topics. In this paper, we propose to design and develop a mobile security labware with smart mobile devices to promote the cybersecurity education. The integration of mobile computing technologies and smart devices into cybersecurity education will connect the education to leading-edge information technologies, motivate and engage students in security learning, fill in the gap with IT industry need, and help faculties build expertise on mobile computing. In addition, the hands-on experience with mobile app development will promote student learning and supply them with a better understanding of security knowledge not only in classical security domains but also in the emerging mobile security areas.

Laato, Samuli, Farooq, Ali, Tenhunen, Henri, Pitkamaki, Tinja, Hakkala, Antti, Airola, Antti.  2020.  AI in Cybersecurity Education- A Systematic Literature Review of Studies on Cybersecurity MOOCs. 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT). :6—10.

Machine learning (ML) techniques are changing both the offensive and defensive aspects of cybersecurity. The implications are especially strong for privacy, as ML approaches provide unprecedented opportunities to make use of collected data. Thus, education on cybersecurity and AI is needed. To investigate how AI and cybersecurity should be taught together, we look at previous studies on cybersecurity MOOCs by conducting a systematic literature review. The initial search resulted in 72 items and after screening for only peer-reviewed publications on cybersecurity online courses, 15 studies remained. Three of the studies concerned multiple cybersecurity MOOCs whereas 12 focused on individual courses. The number of published work evaluating specific cybersecurity MOOCs was found to be small compared to all available cybersecurity MOOCs. Analysis of the studies revealed that cybersecurity education is, in almost all cases, organised based on the topic instead of used tools, making it difficult for learners to find focused information on AI applications in cybersecurity. Furthermore, there is a gab in academic literature on how AI applications in cybersecurity should be taught in online courses.

Sabillon, Regner, Serra-Ruiz, Jordi, Cavaller, Victor, Cano, Jeimy.  2017.  A Comprehensive Cybersecurity Audit Model to Improve Cybersecurity Assurance: The CyberSecurity Audit Model (CSAM). 2017 International Conference on Information Systems and Computer Science (INCISCOS). :253—259.

Nowadays, private corporations and public institutions are dealing with constant and sophisticated cyberthreats and cyberattacks. As a general warning, organizations must build and develop a cybersecurity culture and awareness in order to defend against cybercriminals. Information Technology (IT) and Information Security (InfoSec) audits that were efficient in the past, are trying to converge into cybersecurity audits to address cyber threats, cyber risks and cyberattacks that evolve in an aggressive cyber landscape. However, the increase in number and complexity of cyberattacks and the convoluted cyberthreat landscape is challenging the running cybersecurity audit models and putting in evidence the critical need for a new cybersecurity audit model. This article reviews the best practices and methodologies of global leaders in the cybersecurity assurance and audit arena. By means of the analysis of the current approaches and theoretical background, their real scope, strengths and weaknesses are highlighted looking forward a most efficient and cohesive synthesis. As a resut, this article presents an original and comprehensive cybersecurity audit model as a proposal to be utilized for conducting cybersecurity audits in organizations and Nation States. The CyberSecurity Audit Model (CSAM) evaluates and validates audit, preventive, forensic and detective controls for all organizational functional areas. CSAM has been tested, implemented and validated along with the Cybersecurity Awareness TRAining Model (CATRAM) in a Canadian higher education institution. A research case study is being conducted to validate both models and the findings will be published accordingly.

Addae, Joyce, Radenkovic, Milena, Sun, Xu, Towey, Dave.  2016.  An extended perspective on cybersecurity education. 2016 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). :367—369.
The current trend of ubiquitous device use whereby computing is becoming increasingly context-aware and personal, has created a growing concern for the protection of personal privacy. Privacy is an essential component of security, and there is a need to be able to secure personal computers and networks to minimize privacy depreciation within cyberspace. Human error has been recognized as playing a major role in security breaches: Hence technological solutions alone cannot adequately address the emerging security and privacy threats. Home users are particularly vulnerable to cybersecurity threats for a number of reasons, including a particularly important one that our research seeks to address: The lack of cybersecurity education. We argue that research seeking to address the human element of cybersecurity should not be limited only to the design of more usable technical security mechanisms, but should be extended and applied to offering appropriate training to all stakeholders within cyberspace.
Alnsour, Rawan, Hamdan, Basil.  2020.  Incorporating SCADA Cybersecurity in Undergraduate Engineering Technology Information Technology Education. 2020 Intermountain Engineering, Technology and Computing (IETC). :1—4.

The purpose of this paper is threefold. First, it makes the case for incorporating cybersecurity principles into undergraduate Engineering Technology Education and for incorporating Industrial Control Systems (ICS) principles into undergraduate Information Technology (IT)/Cybersecurity Education. Specifically, the paper highlights the knowledge/skill gap between engineers and IT/Cybersecurity professionals with respect to the cybersecurity of the ICS. Secondly, it identifies several areas where traditional IT systems and ICS intercept. This interception not only implies that ICS are susceptible to the same cyber threats as traditional IT/IS but also to threats that are unique to ICS. Subsequently, the paper identifies several areas where cybersecurity principles can be applied to ICS. By incorporating cybersecurity principles into Engineering Technology Education, the paper hopes to provide IT/Cybersecurity and Engineering Students with (a) the theoretical knowledge of the cybersecurity issues associated with administering and operating ICS and (b) the applied technical skills necessary to manage and mitigate the cyber risks against these systems. Overall, the paper holds the promise of contributing to the ongoing effort aimed at bridging the knowledge/skill gap with respect to securing ICS against cyber threats and attacks.

Peck, Sarah Marie, Khan, Mohammad Maifi Hasan, Fahim, Md Abdullah Al, Coman, Emil N, Jensen, Theodore, Albayram, Yusuf.  2020.  Who Would Bob Blame? Factors in Blame Attribution in Cyberattacks Among the Non-Adopting Population in the Context of 2FA 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :778–789.
This study focuses on identifying the factors contributing to a sense of personal responsibility that could improve understanding of insecure cybersecurity behavior and guide research toward more effective messaging targeting non-adopting populations. Towards that, we ran a 2(account type) x2(usage scenario) x2(message type) between-group study with 237 United States adult participants on Amazon MTurk, and investigated how the non-adopting population allocates blame, and under what circumstances they blame the end user among the parties who hold responsibility: the software companies holding data, the attackers exposing data, and others. We find users primarily hold service providers accountable for breaches but they feel the same companies should not enforce stronger security policies on users. Results indicate that people do hold end users accountable for their behavior in the event of a breach, especially when the users' behavior affects others. Implications of our findings in risk communication is discussed in the paper.
Tang, Sirui, Liu, Zhaoxi, Wang, Lingfeng.  2020.  Power System Reliability Analysis Considering External and Insider Attacks on the SCADA System. 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T D). :1—5.

Cybersecurity of the supervisory control and data acquisition (SCADA) system, which is the key component of the cyber-physical systems (CPS), is facing big challenges and will affect the reliability of the smart grid. System reliability can be influenced by various cyber threats. In this paper, the reliability of the electric power system considering different cybersecurity issues in the SCADA system is analyzed by using Semi-Markov Process (SMP) and mean time-to-compromise (MTTC). External and insider attacks against the SCADA system are investigated with the SMP models and the results are compared. The system reliability is evaluated by reliability indexes including loss of load probability (LOLP) and expected energy not supplied (EENS) through Monte Carlo Simulations (MCS). The lurking threats of the cyberattacks are also analyzed in the study. Case studies were conducted on the IEEE Reliability Test System (RTS-96). The results show that with the increase of the MTTCs of the cyberattacks, the LOLP values decrease. When insider attacks are considered, both the LOLP and EENS values dramatically increase owing to the decreased MTTCs. The results provide insights into the establishment of the electric power system reliability enhancement strategies.

Piplai, A., Ranade, P., Kotal, A., Mittal, S., Narayanan, S. N., Joshi, A..  2020.  Using Knowledge Graphs and Reinforcement Learning for Malware Analysis. 2020 IEEE International Conference on Big Data (Big Data). :2626—2633.

Machine learning algorithms used to detect attacks are limited by the fact that they cannot incorporate the back-ground knowledge that an analyst has. This limits their suitability in detecting new attacks. Reinforcement learning is different from traditional machine learning algorithms used in the cybersecurity domain. Compared to traditional ML algorithms, reinforcement learning does not need a mapping of the input-output space or a specific user-defined metric to compare data points. This is important for the cybersecurity domain, especially for malware detection and mitigation, as not all problems have a single, known, correct answer. Often, security researchers have to resort to guided trial and error to understand the presence of a malware and mitigate it.In this paper, we incorporate prior knowledge, represented as Cybersecurity Knowledge Graphs (CKGs), to guide the exploration of an RL algorithm to detect malware. CKGs capture semantic relationships between cyber-entities, including that mined from open source. Instead of trying out random guesses and observing the change in the environment, we aim to take the help of verified knowledge about cyber-attack to guide our reinforcement learning algorithm to effectively identify ways to detect the presence of malicious filenames so that they can be deleted to mitigate a cyber-attack. We show that such a guided system outperforms a base RL system in detecting malware.

Giannoutakis, K. M., Spathoulas, G., Filelis-Papadopoulos, C. K., Collen, A., Anagnostopoulos, M., Votis, K., Nijdam, N. A..  2020.  A Blockchain Solution for Enhancing Cybersecurity Defence of IoT. 2020 IEEE International Conference on Blockchain (Blockchain). :490—495.

The growth of IoT devices during the last decade has led to the development of smart ecosystems, such as smart homes, prone to cyberattacks. Traditional security methodologies support to some extend the requirement for preserving privacy and security of such deployments, but their centralized nature in conjunction with low computational capabilities of smart home gateways make such approaches not efficient. Last achievements on blockchain technologies allowed the use of such decentralized architectures to support cybersecurity defence mechanisms. In this work, a blockchain framework is presented to support the cybersecurity mechanisms of smart homes installations, focusing on the immutability of users and devices that constitute such environments. The proposed methodology provides also the appropriate smart contracts support for ensuring the integrity of the smart home gateway and IoT devices, as well as the dynamic and immutable management of blocked malicious IPs. The framework has been deployed on a real smart home environment demonstrating its applicability and efficiency.

Saganowski, S..  2020.  A Three-Stage Machine Learning Network Security Solution for Public Entities. 2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). :1097–1104.
In the era of universal digitization, ensuring network and data security is extremely important. As a part of the Regional Center for Cybersecurity initiative, a three-stage machine learning network security solution is being developed and will be deployed in March 2021. The solution consists of prevention, monitoring, and curation stages. As prevention, we utilize Natural Language Processing to extract the security-related information from social media, news portals, and darknet. A deep learning architecture is used to monitor the network in real-time and detect any abnormal traffic. A combination of regular expressions, pattern recognition, and heuristics are applied to the abuse reports to automatically identify intrusions that passed other security solutions. The lessons learned from the ongoing development of the system, alongside the results, extensive analysis, and discussion is provided. Additionally, a cybersecurity-related corpus is described and published within this work.
Yermalovich, P., Mejri, M..  2020.  Information security risk assessment based on decomposition probability via Bayesian Network. 2020 International Symposium on Networks, Computers and Communications (ISNCC). :1–8.
Well-known approaches to risk analysis suggest considering the level of an information system risk as one frame in a film. This means that we only can perform a risk assessment for the current point in time. This article explores the idea of risk assessment in a future period, as a prediction of what we will see in the film later. In other words, the article presents an approach to predicting a potential future risk and suggests the idea of relying on forecasting the likelihood of an attack on information system assets. To establish the risk level at a selected time interval in the future, one has to perform a mathematical decomposition. To do this, we need to select the required information system parameters for the predictions and their statistical data for risk assessment. This method can be used to ensure more detailed budget planning when ensuring the protection of the information system. It can be also applied in case of a change of the information protection configuration to satisfy the accepted level of risk associated with projected threats and vulnerabilities.
Lyshevski, S. E., Aved, A., Morrone, P..  2020.  Information-Centric Cyberattack Analysis and Spatiotemporal Networks Applied to Cyber-Physical Systems. 2020 IEEE Microwave Theory and Techniques in Wireless Communications (MTTW). 1:172—177.

Cyber-physical systems (CPS) depend on cybersecurity to ensure functionality, data quality, cyberattack resilience, etc. There are known and unknown cyber threats and attacks that pose significant risks. Information assurance and information security are critical. Many systems are vulnerable to intelligence exploitation and cyberattacks. By investigating cybersecurity risks and formal representation of CPS using spatiotemporal dynamic graphs and networks, this paper investigates topics and solutions aimed to examine and empower: (1) Cybersecurity capabilities; (2) Information assurance and system vulnerabilities; (3) Detection of cyber threat and attacks; (4) Situational awareness; etc. We introduce statistically-characterized dynamic graphs, novel entropy-centric algorithms and calculi which promise to ensure near-real-time capabilities.

Abbas, H., Suguri, H., Yan, Z., Allen, W., Hei, X. S..  2020.  IEEE Access Special Section: Security Analytics and Intelligence for Cyber Physical Systems. IEEE Access. 8:208195—208198.

A Cyber Physical System (CPS) is a smart network system with actuators, embedded sensors, and processors to interact with the physical world by guaranteeing the performance and supporting real-time operations of safety critical applications. These systems drive innovation and are a source of competitive advantage in today’s challenging world. By observing the behavior of physical processes and activating actions, CPS can alter its behavior to make the physical environment perform better and more accurately. By definition, CPS basically has two major components including cyber systems and physical processes. Examples of CPS include autonomous transportation systems, robotics systems, medical monitoring, automatic pilot avionics, and smart grids. Advances in CPS will empower scalability, capability, usability, and adaptability, which will go beyond the simple systems of today. At the same time, CPS has also increased cybersecurity risks and attack surfaces. Cyber attackers can harm such systems from multiple sources while hiding their identities. As a result of sophisticated threat matrices, insufficient knowledge about threat patterns, and industrial network automation, CPS has become extremely insecure. Since such infrastructure is networked, attacks can be prompted easily without much human participation from remote locations, thereby making CPS more vulnerable to sophisticated cyber-attacks. In turn, large-scale data centers managing a huge volume of CPS data become vulnerable to cyber-attacks. To secure CPS, the role of security analytics and intelligence is significant. It brings together huge amounts of data to create threat patterns, which can be used to prevent cyber-attacks in a timely fashion. The primary objective of this Special Section in IEEE A CCESS is to collect a complementary and diverse set of articles, which demonstrate up-to-date information and innovative developments in the domain of security analytics and intelligence for CPS.

Baybulatov, A. A., Promyslov, V. G..  2020.  On a Deterministic Approach to Solving Industrial Control System Problems. 2020 International Russian Automation Conference (RusAutoCon). :115—120.

Since remote ages, queues and delays have been a rather exasperating reality of human daily life. Today, they pursue us everywhere: in technical, social, socio-technical, and even control systems, dramatically deteriorating their performance. In this variety, it is the computer systems that are sure to cause the growing anxiety in our digital era. Although for our everyday Internet surfing, experiencing long-lasting and annoying delays is an unpleasant but not dangerous situation, for industrial control systems, especially those dealing with critical infrastructures, such behavior is unacceptable. The article presents a deterministic approach to solving some digital control system problems associated with delays and backlogs. Being based on Network calculus, in contrast to statistical methods of Queuing theory, it provides worst-case results, which are eminently desirable for critical infrastructures. The article covers the basics of a theory of deterministic queuing systems Network calculus, its evolution regarding the relationship between backlog bound and delay, and a technique for handling empirical data. The problems being solved by the deterministic approach: standard calculation of network performance measures, estimation of database maximum updating time, and cybersecurity assessment including such issues as the CIA triad representation, operational technology influence, and availability understanding focusing on its correlation with a delay are thoroughly discussed as well.

Khan, W. Z., Arshad, Q.-u-A., Hakak, S., Khan, M. K., Saeed-Ur-Rehman.  2020.  Trust Management in Social Internet of Things: Architectures, Recent Advancements and Future Challenges. IEEE Internet of Things Journal. :1—1.

Social Internet of Things (SIoT) is an extension of Internet of Things (IoT) that converges with Social networking concepts to create Social networks of interconnected smart objects. This convergence allows the enrichment of the two paradigms, resulting into new ecosystems. While IoT follows two interaction paradigms, human-to-human (H2H) and thing-to-thing (T2T), SIoT adds on human-to-thing (H2T) interactions. SIoT enables smart “Social objects” that intelligently mimic the social behavior of human in the daily life. These social objects are equipped with social functionalities capable of discovering other social objects in the surroundings and establishing social relationships. They crawl through the social network of objects for the sake of searching for services and information of interest. The notion of trust and trustworthiness in social communities formed in SIoT is still new and in an early stage of investigation. In this paper, our contributions are threefold. First, we present the fundamentals of SIoT and trust concepts in SIoT, clarifying the similarities and differences between IoT and SIoT. Second, we categorize the trust management solutions proposed so far in the literature for SIoT over the last six years and provide a comprehensive review. We then perform a comparison of the state of the art trust management schemes devised for SIoT by performing comparative analysis in terms of trust management process. Third, we identify and discuss the challenges and requirements in the emerging new wave of SIoT, and also highlight the challenges in developing trust and evaluating trustworthiness among the interacting social objects.

Foroughi, F., Hadipour, H., Shafiee, A. M..  2020.  High-Performance Monitoring Sensors for Home Computer Users Security Profiling. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1—7.

Recognising user's risky behaviours in real-time is an important element of providing appropriate solutions and recommending suitable actions for responding to cybersecurity threats. Employing user modelling and machine learning can make this process automated by requires high-performance intelligent agent to create the user security profile. User profiling is the process of producing a profile of the user from historical information and past details. This research tries to identify the monitoring factors and suggests a novel observation solution to create high-performance sensors to generate the user security profile for a home user concerning the user's privacy. This observer agent helps to create a decision-making model that influences the user's decision following real-time threats or risky behaviours.

Ashiku, L., Dagli, C..  2020.  Agent Based Cybersecurity Model for Business Entity Risk Assessment. 2020 IEEE International Symposium on Systems Engineering (ISSE). :1—6.

Computer networks and surging advancements of innovative information technology construct a critical infrastructure for network transactions of business entities. Information exchange and data access though such infrastructure is scrutinized by adversaries for vulnerabilities that lead to cyber-attacks. This paper presents an agent-based system modelling to conceptualize and extract explicit and latent structure of the complex enterprise systems as well as human interactions within the system to determine common vulnerabilities of the entity. The model captures emergent behavior resulting from interactions of multiple network agents including the number of workstations, regular, administrator and third-party users, external and internal attacks, defense mechanisms for the network setting, and many other parameters. A risk-based approach to modelling cybersecurity of a business entity is utilized to derive the rate of attacks. A neural network model will generalize the type of attack based on network traffic features allowing dynamic state changes. Rules of engagement to generate self-organizing behavior will be leveraged to appoint a defense mechanism suitable for the attack-state of the model. The effectiveness of the model will be depicted by time-state chart that shows the number of affected assets for the different types of attacks triggered by the entity risk and the time it takes to revert into normal state. The model will also associate a relevant cost per incident occurrence that derives the need for enhancement of security solutions.

Ben-Yaakov, Y., Meyer, J., Wang, X., An, B..  2020.  User detection of threats with different security measures. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—6.

Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.

Ganfure, G. O., Wu, C.-F., Chang, Y.-H., Shih, W.-K..  2020.  DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1—6.

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

Meshkat, L., Miller, R. L., Hillsgrove, C., King, J..  2020.  Behavior Modeling for Cybersecurity. 2020 Annual Reliability and Maintainability Symposium (RAMS). :1—7.

A significant percentage of cyber security incidents can be prevented by changing human behaviors. The humans in the loop include the system administrators, software developers, end users and the personnel responsible for securing the system. Each of these group of people work in a given context and are affected by both soft factors such as management influences and workload and more tangible factors in the real world such as errors in procedures and scanning devices, faulty code or the usability of the systems they work with.

Faith, B. Fatokun, Hamid, S., Norman, A., Johnson, O. Fatokun, Eke, C. I..  2020.  Relating Factors of Tertiary Institution Students’ Cybersecurity Behavior. 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS). :1—6.

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution students undergo lot of cybersecurity issues due to their constant Internet exposure, however there is a lack in literature with regards to tertiary institution students' cybersecurity behaviors. This research aimed at linking the factors responsible for tertiary institutions students' cybersecurity behavior, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behavior (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); Familiarity with Cyber-Threats (FCT), thus exploring the relationship between the factors and the students' Cybersecurity Behaviors (CSB). A cross-sectional online survey was used to gather data from 450 undergraduate and postgraduate students from tertiary institutions within Klang Valley, Malaysia. Correlation Analysis was used to find the relationships existing among the cybersecurity behavioral factors via SPSS version 25. Results indicate that all factors were significantly related to the cybersecurity behaviors of the students apart from Perceived Severity. Practically, the study instigates the need for more cybersecurity training and practices in the tertiary institutions.

Shah, P. R., Agarwal, A..  2020.  Cybersecurity Behaviour of Smartphone Users Through the Lens of Fogg Behaviour Model. 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). :79—82.

It is now a fact that human is the weakest link in the cybersecurity chain. Many theories from behavioural science like the theory of planned behaviour and protection motivation theory have been used to investigate the factors that affect the cybersecurity behaviour and practices of the end-user. In this paper, the researchers have used Fogg behaviour model (FBM) to study factors affecting the cybersecurity behaviour and practices of smartphone users. This study found that the odds of secure behaviour and practices by respondents with high motivation and high ability were 4.64 times more than the respondents with low motivation and low ability. This study describes how FBM may be used in the design and development of cybersecurity awareness program leading to a behaviour change.

Shaout, A., Schmidt, N..  2020.  Keystroke Identifier Using Fuzzy Logic to Increase Password Security. 2020 21st International Arab Conference on Information Technology (ACIT). :1—8.

Cybersecurity is a major issue today. It is predicted that cybercrime will cost the world \$6 trillion annually by 2021. It is important to make logins secure as well as to make advances in security in order to catch cybercriminals. This paper will design and create a device that will use Fuzzy logic to identify a person by the rhythm and frequency of their typing. The device will take data from a user from a normal password entry session. This data will be used to make a Fuzzy system that will be able to identify the user by their typing speed. An application of this project could be used to make a more secure log-in system for a user. The log-in system would not only check that the correct password was entered but also that the rhythm of how the password was typed matched the user. Another application of this system could be used to help catch cybercriminals. A cybercriminal may have a certain rhythm at which they type at and this could be used like a fingerprint to help officials locate cybercriminals.

Sayers, J. M., Feighery, B. E., Span, M. T..  2020.  A STPA-Sec Case Study: Eliciting Early Security Requirements for a Small Unmanned Aerial System. 2020 IEEE Systems Security Symposium (SSS). :1—8.

This work describes a top down systems security requirements analysis approach for understanding and eliciting security requirements for a notional small unmanned aerial system (SUAS). More specifically, the System-Theoretic Process Analysis approach for Security (STPA-Sec) is used to understand and elicit systems security requirements. The effort employs STPA-Sec on a notional SUAS system case study to detail the development of functional-level security requirements, design-level engineering considerations, and architectural-level security specification criteria early in the system life cycle when the solution trade-space is largest rather than merely examining components and adding protections during system operation or sustainment. These details were elaborated during a semester independent study research effort by two United States Air Force Academy Systems Engineering cadets, guided by their instructor and a series of working group sessions with UAS operators and subject matter experts. This work provides insight into a viable systems security requirements analysis approach which results in traceable security, safety, and resiliency requirements that can be designed-for, built-to, and verified with confidence.