Cybersecurity Risk Assessment Research Articles

Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention

The abstract is "The rapid evolution of cyber threats necessitates innovative defenses, particularly in the domains of risk assessment and fraud detection. This paper explores the integration of Artificial Intelligence (AI) and Adversarial Machine Learning (ML) techniques as a formidable strategy against increasingly sophisticated cyber-attacks. We present a comprehensive framework that leverages AI to dynamically assess cybersecurity risks and detect fraudulent activities with unprecedented accuracy and speed. Firstly, we delve into the foundational principles of adversarial machine learning, outlining how these techniques can be employed to simulate potential cyber threats, thereby enabling the development of more resilient AI-driven cybersecurity systems. We highlight the dual role of adversarial ML in both enhancing security defenses and potentially serving as a vector for sophisticated attacks, underscoring the importance of developing robust, adversarial-resistant models. Subsequently, we introduce a novel adaptive risk assessment methodology that incorporates real-time data analysis, machine learning algorithms, and predictive modeling to accurately identify and prioritize threats. This method adapts to the evolving digital landscape, ensuring that cybersecurity measures are always one step ahead of potential attackers. In the context of fraud detection, we explore -how AI algorithms can sift through vast datasets to detect anomalies and patterns indicative of fraudulent behavior. Through case studies and empirical analysis, we demonstrate the effectiveness of AI in identifying fraud across various sectors, from financial transactions to online identity verification processes. Our research contributes to the cybersecurity field by providing a detailed examination of how AI and adversarial ML can be harnessed to fortify digital defenses, improve risk assessment techniques, and enhance fraud detection capabilities. The insights garnered from this study not only advance theoretical understanding but also offer practical guidance for organizations seeking to implement AI-driven security solutions. As cyber threats continue to evolve, the integration of AI and adversarial ML in cybersecurity strategies will be paramount in safeguarding digital assets and maintaining the integrity of online systems."

Systematic Risk Characterisation of Hardware Threats to Automotive System

The increasing dependence of modern automotive systems on electronics and software poses cybersecurity risks previously not factored into design and engineering of such systems. Attacks on hardware components, communication modules and embedded software – many of which are purposefully designed for automotive control and communications – are the key focus of this paper. We adopt a novel approach to characterise such attacks using Gajski-Kuhn Y-charts to represent attack manipulation across behavioural, structural and physical domains. Our selection of attacks is evidence-driven demonstrating threats that have been demonstrated to be feasible in the real-world. We then risk assess impact of such threats using the recently adopted ISO/SAE 21434 standard for automotive cybersecurity risk assessment, including mitigations for potential adoption. Our work serves to provide unique insights into the complex dynamic of hardware vulnerabilities and how the industry may address system-level security and protection of modern automotive platforms.

BWM Integrated VIKOR method using Neutrosophic fuzzy sets for cybersecurity risk assessment of connected and autonomous vehicles

Connected and autonomous vehicles (CAVs) have gained significant importance in intelligent transportation systems. However, CAVs are associated with significant cyber-risks caused by different threat-agents, which require robust mitigation strategies for secure operation. Different categories of threat-agents come with varying resources, motivation, and skills that must be addressed effectively for the secure operation of CAVs. This study offers a comprehensive analysis of cybersecurity risks from different categories of threat-agents targeting CAVs. A novel hybrid multi-criteria decision making (MCDM) technique has been developed to prioritize the threat-agent categories as part of the cyber defense mechanism. In this study, the major threat-agent categories are identified from the existing literature and cybersecurity reports. Then expert opinion is collected on the likelihood and severity of cyber-attacks undertaken by different threat-agent categories. Single-valued neutrosophic fuzzy sets (SVNFS) are implemented to consolidate the subjectivity of the linguistic opinions given by the experts. Then Best-Worst Method (BWM) is used to determine the relative criteria weightage on the cyber-attack consequences. These weights are then supplied to the ViseKriterijum-ska Optimizacija I Kompromisno Resenj (VIKOR) method for ranking the categories of threat-agents based on the risk perceived by the experts. A case study is presented with five categories of threat-agents and five loss criteria targeting CAVs to validate the viability of the proposed model. The result shows that insider attackers pose maximum risk for cybersecurity of CAVs. The proposed technique will provide helpful insights to the decision-makers for formulating effective defense strategies against cyber-attackers.

The Role of Internal Auditors Characteristics in Cybersecurity Risk Assessment in Financial-Based Business Organisations: a Conceptual Review

Purpose: This paper aims to establish a theoretical framework that will enhance the examination of the role of internal auditors in cybersecurity risk assessment in financial-based business organizations. Financial-based business organizations are institutions or companies that render financial services to public and private stakeholders in an economy. It is a powerful sector in the economy of every country. This drive poses a lot of challenges to organizations. Hence, business organizations strategically devised a means to safeguard the integrity, confidentiality, and availability of information. Also, innovation poses many risks and threats to the internal audit function in an organization. Theoretical Framework/Findings: Using the competency and planned behaviour theories (McClelland 1973 and Ajzen,1991), this study disclosed that the task performance of cybersecurity risk assessment by the internal auditor is influenced by the required internal auditor’s characteristics of professional ethics of integrity and objectivity, personality traits, professional skills competency professional knowledge competency and deterrence and rewards to advise the management on the implications of cyber security risk on business organisations for monitoring and mitigations. Methodology: A literature review approach is adopted to highlight the role of internal auditors in cyber security risk assessment in financial–based business organizations. Research Limitation/Implication: This conceptual paper has consequences for the practice of internal auditing. This approach is helpful to academic scholars in testing it out in the real world. This model is helpful to practitioners when evaluating the function of IAs in the cybersecurity risk assessment context. Originality/Values: Earlier auditing-related studies haven't addressed this problem. This study makes an effort to close such a gap and investigate the subject of the internal auditor’s characteristics and cyber security risk assessment among financial-based organizations.

Improving Risk Assessment Model for Cyber Security Using Robust Aggregation Operators for Bipolar Complex Fuzzy Soft Inference Systems

Improving a risk assessment technique for the problem of cyber security is required to modify the technique’s capability to identify, evaluate, assess, and mitigate potential cyber threats and ambiguities. The major theme of this paper is to find the best strategy to improve and refine the cyber security risk assessment model. For this, we compute some operational laws for bipolar complex fuzzy soft (BCFS) sets and then propose the BCFS weighted averaging (BCFSWA) operator, BCFS ordered weighted averaging (BCFSOWA) operator, BCFS weighted geometric (BCFSWG) operator, and BCFS ordered weighted geometric (BCFSOWG) operator. Furthermore, we give their properties, such as idempotency, monotonicity, and boundedness. Additionally, we improve the risk assessment technique for the cyber security model based on the proposed operators. We illustrate the technique of multi-attribute decision-making (MADM) problems for the derived operators based on BCFS information. Finally, we compare our ranking results with those of some existing operators for evaluating and addressing the supremacy, validity, and efficiency of these operators under BCFS information.

Taxonomy-Driven Graph-Theoretic Framework for Manufacturing Cybersecurity Risk Modeling and Assessment

Abstract Identifying, analyzing, and evaluating cybersecurity risks are essential to devise effective decision-making strategies to secure critical manufacturing against potential cyberattacks. However, a manufacturing-specific quantitative approach is lacking to effectively model threat events and evaluate the unique cybersecurity risks in discrete manufacturing systems. In response, this paper introduces the first taxonomy-driven graph-theoretic model and framework to formally represent this unique cybersecurity threat landscape and identify vulnerable manufacturing assets requiring prioritized control. First, the proposed framework characterizes threat actors’ techniques, tactics, and procedures using taxonomical classifications of manufacturing-specific threat attributes and integrates these attributes into cybersecurity risk modeling. This facilitates the systematic generation of comprehensive and generalizable cyber-physical attack graphs for discrete manufacturing systems. Second, using the attack graph formalism, the proposed framework enables concurrent modeling and analysis of a wide variety of cybersecurity threats comprising varying attack vectors, locations, vulnerabilities, and consequences. The risk model captures the cascading attack impact of varying attack methods through different cyber and physical entities in manufacturing systems, leading to specific consequences. Then, the constructed cyber-physical attack graphs are analyzed to comprehend threat propagation through the discrete manufacturing value chain and identify potential attack paths. Third, a quantitative risk assessment approach is presented to evaluate the cybersecurity risk associated with potential attack paths. It also identifies the attack path with the maximum likelihood of success, pointing out critical manufacturing assets requiring prioritized control. Finally, the proposed risk modeling and assessment framework is demonstrated using an illustrative example.

Integrated Neutrosophic methodology and Machine Learning Models for Cybersecurity Risk Assessment: An exploratory study

Information technology security, or Cybersecurity, guards against hostile cyberattacks on computers, mobile devices, servers, electronic systems, and networks. Cybersecurity risks have been a significant concern for any vital digital infrastructure in recent years, and different online cyberattacks are also becoming a significant problem for society. Consequently, it's critical to adopt technology created to provide cybersecurity. However, one should consider the associated hazards while selecting among Cybersecurity systems. We have developed a multi-criteria decision-making (MCDM) approach based on a single-valued neutrosophic set (SVNS). This allows specialists more latitude in assessing the criteria and alternatives using language and overcoming uncertain information. The VIKOR is an MCDM methodology used to rank the other options. The VIKOR method is integrated with the neutrosophic set. There are 18 criteria, and 10 alternatives are used in this study. The sensitivity analysis and comparative analysis are conducted in this study. The sensitivity analysis results show the alternatives' rank is stable under different cases. The comparative analysis compares the suggested method with other MCDM methods. The comparative analysis shows the suggested method was effective compared with other MCDM methods. Machine learning methods predict the type of attack in Cybersecurity. This study uses Three machine learning methods: decision tree, random forest, and support vector machine.

CYBERSECURITY RISK ASSESSMENT IN BANKING: METHODOLOGIES AND BEST PRACTICES

Cybersecurity risk assessment in banking is the process of identifying, analyzing, and evaluating the cyber threats and vulnerabilities that may affect the confidentiality, integrity, and availability of the information systems and data of banks and their customers. Cybersecurity risk assessment in banking helps banks to prioritize and implement appropriate controls and measures to mitigate the cyber risks and to comply with the relevant regulations and standards. This study focusses on identifying effective risk assessment strategies, highlighting how they can be adapted and applied in various banking environments, especially in developing economies like Nigeria. As the banking industry continues to evolve in the digital era, the significance of robust cybersecurity measures cannot be overstated. This paper delves into the critical domain of Cybersecurity Risk Assessment in Banking, exploring various methodologies and best practices employed to safeguard financial institutions against evolving cyber threats. The dynamic landscape of cyber risks faced by banks, ranging from sophisticated malware and phishing attacks to insider threats and system vulnerabilities are examined. The paper provides an in-depth analysis of established and emerging methodologies for conducting effective cybersecurity risk assessments in the banking sector. It explores quantitative and qualitative risk assessment approaches, threat modeling, and scenario analysis, shedding light on their respective strengths and limitations. Moreover, the document highlights the importance of aligning risk assessment methodologies with industry regulations and compliance standards to ensure a comprehensive and regulatory-compliant cybersecurity framework. Best practices for cybersecurity risk management in banking are scrutinized, emphasizing the integration of proactive threat intelligence, continuous monitoring, and incident response planning. The role of advanced technologies, including artificial intelligence and machine learning, in enhancing the efficiency of risk assessment processes is also discussed. Furthermore, the paper addresses the human element in cybersecurity, emphasizing the significance of training and awareness programs to mitigate risks associated with human error and social engineering attacks. By synthesizing insights from industry practices, regulatory guidelines, and technological advancements, this paper offers a comprehensive guide for banking professionals, cybersecurity practitioners, and policymakers involved in fortifying the resilience of financial institutions against cyber threats. Ultimately, the research aims to contribute to the ongoing discourse on cybersecurity risk assessment in banking, providing actionable insights to navigate the complex landscape of digital risks and ensuring the continued trust and security of the financial ecosystem.
 Keywords: Cybersecurity; Risk Assessment; Banking; Methodologies; Cyber Threat; Artificial Intelligence; Best Practices

A Hybrid Methodology to Assess Cyber Resilience of IoT in Energy Management and Connected Sites.

Cyber threats and vulnerabilities present an increasing risk to the safe and frictionless execution of business operations. Bad actors ("hackers"), including state actors, are increasingly targeting the operational technologies (OTs) and industrial control systems (ICSs) used to protect critical national infrastructure (CNI). Minimisations of cyber risk, attack surfaces, data immutability, and interoperability of IoT are some of the main challenges of today's CNI. Cyber security risk assessment is one of the basic and most important activities to identify and quantify cyber security threats and vulnerabilities. This research presents a novel i-TRACE security-by-design CNI methodology that encompasses CNI key performance indicators (KPIs) and metrics to combat the growing vicarious nature of remote, well-planned, and well-executed cyber-attacks against CNI, as recently exemplified in the current Ukraine conflict (2014-present) on both sides. The proposed methodology offers a hybrid method that specifically identifies the steps required (typically undertaken by those responsible for detecting, deterring, and disrupting cyber attacks on CNI). Furthermore, we present a novel, advanced, and resilient approach that leverages digital twins and distributed ledger technologies for our chosen i-TRACE use cases of energy management and connected sites. The key steps required to achieve the desired level of interoperability and immutability of data are identified, thereby reducing the risk of CNI-specific cyber attacks and minimising the attack vectors and surfaces. Hence, this research aims to provide an extra level of safety for CNI and OT human operatives, i.e., those tasked with and responsible for detecting, deterring, disrupting, and mitigating these cyber-attacks. Our evaluations and comparisons clearly demonstrate that i-TRACE has significant intrinsic advantages compared to existing "state-of-the-art" mechanisms.

Cybersecurity Risk Assessments within Critical Infrastructure Social Networks

Cybersecurity social networking is a new scientific and engineering discipline that was interdisciplinary in its early days, but is now transdisciplinary. The issues of reviewing and analyzing of principal tasks related to information collection, monitoring of social networks, assessment methods, and preventing and combating cybersecurity threats are, therefore, essential and pending. There is a need to design certain methods, models, and program complexes aimed at estimating risks related to the cyberspace of social networks and the support of their activities. This study considers a risk to be the combination of consequences of a given event (or incident) with a probable occurrence (likelihood of occurrence) involved, while risk assessment is a general issue of identification, estimation, and evaluation of risk. The findings of the study made it possible to elucidate that the technique of cognitive modeling for risk assessment is part of a comprehensive cybersecurity approach included in the requirements of basic IT standards, including IT security risk management. The study presents a comprehensive approach in the field of cybersecurity in social networks that allows for consideration of all the elements that constitute cybersecurity as a complex, interconnected system. The ultimate goal of this approach to cybersecurity is the organization of an uninterrupted scheme of protection against any impacts related to physical, hardware, software, network, and human objects or resources of the critical infrastructure of social networks, as well as the integration of various levels and means of protection.

Integrating NIST and ISO Cybersecurity Audit and Risk Assessment Frameworks into Cameroonian Law

This paper reviews cybersecurity laws and regulations in Cameroon, focusing on cybersecurity and information security audits and risk assessments. The importance of cybersecurity risk assessment and the implementation of security controls to cure deficiencies noted during risk assessments or audits is a critical step in developing cybersecurity resilience. Cameroon's cybersecurity legal framework provides for audits but does not explicitly enumerate controls. Consequently, integrating relevant controls from the NIST frameworks and ISO Standards can improve the cybersecurity posture in Cameroon while waiting for a comprehensive revision of the legal framework. NIST and ISO are internationally recognized as best practices in information security systems and cybersecurity risk management. This paper highlights the lack of specific international law provisions addressing cybersecurity audits and risk assessments. Overall, the paper highlights the importance of continuous risk assessment and monitoring, implementation of security controls, and compliance with organizational policies, relevant laws and regulations to ensure the adequate protection of information systems. Finally, the paper underscores the importance of improving Cameroon's cybersecurity regulations by integrating provisions from NIST and ISO.

Blockchain Technology Applications and Cybersecurity Techniques: A Literature Review

Blockchain is a relatively novice technology rapidly increasing as a next-generation solution for maintaining digital transactions and record-keeping issues. Blockchain technology can be used in financial services, healthcare facilities, social services, the governmental sector, the Internet of Things (IoT), etc. Regularly any new technologies have some challenges like security risks (threats and vulnerabilities). Among these blockchain challenges is managing the large-scale digital transactional system, which is potentially exposed to a multitude of threats. Thus, this paper introduces the categories of blockchain technologies’ cybersecurity vulnerabilities and some aspects of cybersecurity risk assessment.

The Role of Internal Auditors Characteristics in Cybersecurity Risk Assessment in Financial-Based Business Organisations: A Conceptual Review

Purpose: This paper aims to establish a theoretical framework that will enhance the examination of the role of internal auditors in cybersecurity risk assessment in financial-based business organizations. Financial-based business organizations are institutions or companies that render financial services to public and private stakeholders in an economy. It is a powerful sector in the economy of every country. This drive poses a lot of challenges to organizations. Hence, business organizations strategically devised a means to safeguard the integrity, confidentiality, and availability of information. Also, innovation poses many risks and threats to the internal audit function in an organization. Theoretical Framework/Findings: Using the competency and planned behaviour theories (McClelland 1973 and Ajzen,1991), this study disclosed that the task performance of cybersecurity risk assessment by the internal auditor is influenced by the required internal auditor’s characteristics of professional ethics of integrity and objectivity, personality traits, professional skills competency professional knowledge competency and deterrence and rewards to advise the management on the implications of cyber security risk on business organisations for monitoring and mitigations. Methodology: A literature review approach is adopted to highlight the role of internal auditors in cyber security risk assessment in financial–based business organizations. Research Limitation/Implication: This conceptual paper has consequences for the practice of internal auditing. This approach is helpful to academic scholars in testing it out in the real world. This model is helpful to practitioners when evaluating the function of IAs in the cybersecurity risk assessment context. Originality/Values: Earlier auditing-related studies haven't addressed this problem. This study makes an effort to close such a gap and investigate the subject of the internal auditor’s characteristics and cyber security risk assessment among financial-based organizations.

Cyberbiosecurity in high-containment laboratories.

High-containment laboratories (HCLs) conduct critical research on infectious diseases, provide diagnostic services, and produce vaccines for the world's most dangerous pathogens, often called high-consequence pathogens (HCPs). The modernization of HCLs has led to an increasingly cyber-connected laboratory infrastructure. The unique cyberphysical elements of these laboratories and the critical data they generate pose cybersecurity concerns specific to these laboratories. Cyberbiosecurity, the discipline devoted to the study of cybersecurity risks in conjunction with biological risks, is a relatively new field for which few approaches have been developed to identify, assess, and mitigate cyber risks in biological research and diagnostic environments. This study provides a novel approach for cybersecurity risk assessment and identification of risk mitigation measures by applying an asset-impact analysis to the unique environment of HCLs. First, we identified the common cyber and cyberphysical systems in HCLs, summarizing the typical cyber-workflow. We then analyzed the potential adverse outcomes arising from a compromise of these cyber and cyberphysical systems, broadly categorizing potential consequences as relevant to scientific advancement, public health, worker safety, security, and the financial wellbeing of these laboratories. Finally, we discussed potential risk mitigation strategies, leaning heavily on the cybersecurity materials produced by the Center for Internet Security (CIS), including the CIS Controls®, that can serve as a guide for HCL operators to begin the process of implementing risk mitigation measures to reduce their cyberbiorisk and considering the integration of cyber risk management into existing biorisk management practices. This paper provides a discussion to raise awareness among laboratory decision-makers of these critical risks to safety and security within HCLs. Furthermore, this paper can serve as a guide for evaluating cyberbiorisks specific to a laboratory by identifying cyber-connected assets and the impacts associated with a compromise of those assets.

Yet another cybersecurity risk assessment framework

IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsibles face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT risk domain. Furthermore, the existing threat modeling methods have shortcomings. This paper introduces a metamodel-based approach named Yet Another Cybersecurity Risk Assessment Framework (Yacraf). Yacraf aims to enable comprehensive risk assessment for organizations with more decision support. The paper includes a risk calculation formalization and also an example showing how an organization can use and benefit from Yacraf.

Cybersecurity Risk Assessment and Management for Organizational Security

Cybersecurity Risk Assessment and Management for Organizational Security

Design and Implement Machine Learning Tool for Cyber Security Risk Assessment

Design and Implement Machine Learning Tool for Cyber Security Risk Assessment

Cybersecurity Risk Assessment for Medium-Risk Drones: A Systematic Literature Review

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</b> The increased demand for Remotely Piloted Aircraft Systems (RPAS) in Beyond Visual Line-Of-Sight (BVLOS) operations gives rise to a set of concerns regarding cybersecurity that, if not addressed, can lead to the unsafe operation of RPASs. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</b> To assist the airworthiness evaluation that is performed by Civil Aviation Authorities (CAAs), we identified several processes that are used to evaluate the cybersecurity of RPAS. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</b> We conducted a Systematic Literature Review (SLR) by selecting 30 papers (out of 211 screened) that were published during the past five years. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</b> The results of our SLR indicate the importance of cybersecurity to the safe operation of RPAS. It is evident that there is a lack of a systematic process to enable a cybersecurity review of RPAS. We observe that common cyber threats to RPAS are related to jamming, spoofing, and DOS/DDOS (Denial of Service/Distributed Denial of Service). <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusions:</b> Processes relevant to the assessment of RPAS cybersecurity exist, however they differ in safety concerns from our perspective. In addition, with only one exception, the methods have not been used, and/or the use has not been reported as pertaining to industrial application. The most frequently cited vulnerabilities are those related to GPS and datalinks.

Analysis of Cyber Security Threats of the Printing Enterprise

The topic of scientific works on the implementation of modern technologies and systems of automated management of the enterprise, its resources and technical means is analyzed, and the insufficient completeness of research on the features of the integrated approach to the design and deployment of innovative means of production order support. Based on the determined factors of the operation of the enterprise in the latest conditions of the fourth industrial revolution, directions for the formation of strategies for the introduction of the elements of Industry 4.0 in modern printing enterprises, as well as information protection systems, are determined with electronic document circulation. The mechanisms of decision of tasks of management informative risks considered in complex control system by printeries in the conditions of vagueness and at co-operation of elements of control system between itself. The necessity of using a web portal for the formation of printing orders is substantiate, the main components are define and the levels of access to them described. The paper examines the use of classic and gray fuzzy cognitive maps to solve the problem of cyber security risk assessment of the intelligent management system of a printing enterprise. It is demonstrate that the average estimate of local risk, which is formed using an ensemble of two heterogeneous fuzzy cognitive maps, decreases compared to the use of individual cognitive maps. In order tî better, highlight the results of the research, an example of the application of the proposed methodology for assessing the risks of ensuring the integrity of telemetric information in the industrial network of the intelligent technological process management system of a printing enterprise given, with the continuity of the technological process of manufacturing printing products. In addition to the classic FCM, the paradigms of two variants of the FCM extension were also use in the study, namely, the gray FCM, which used to solve the problem of assessing cyber security risks of intelligent management systems of printing enterprises. An analysis of the possibility of building FCM ensembles to increase the effectiveness of risk assessment using several options for formalizing the expert’s knowledge and experience performed. A fragment of the enterprise management system was consider and an analysis of possible directions of attacks on the printing enterprise by malicious software was perform. These are attacks such as replacing the executable files of server and ARM software, overwriting PLC projects during system operation, and refusing to service the equipment. Based on the formed list of attack vectors and the consequences of their implementation, the task of analyzing the risks of cyber security of a printing enterprise, taking into account the impact on the system of possible internal threats, was considered, using the cognitive modeling apparatus as a modeling tool. The scenario of cognitive modeling of the influence of an internal criminal who exploits the vulnerabilities of the software and hardware components of the control system using the given variants of FCM construction is considered. The average assessment of local risks, which formed using an ensemble of cognitive maps, is better from the point of view of dispersion of assessments of the state of target concepts than the use of individual FCMs. The spread of estimates of the state of ensemble concepts is smaller than the spread of estimates of their gray values using the GFCM, on average by 1.4–1.8 times, which indicates a decrease in the influence of the subjectivity factor on the results of risk assessment. The performed scenario modeling showed that the use of the specified means of protection and organizational measures allows reducing the assessment of local risks by 12–18%, which is a significant indicator. This technique allows obtaining a qualitative and quantitative assessment of risk indicators, taking into account the entire set of objective and subjective factors of uncertainty.

A review of cyber security risk assessment for web systems during its deployment and operation

This paper presents the state of the arts in security risk assessment of web systems. The process of assessing security risks and the process of developing and operating information systems in general, web systems in particular, are depicted step by step, showing how the risk assessment is performed during the deployment and the operation of web systems. Based on this analysis, different methods related to the manual and automatic risk assessment are reviewed, focusing on the methods using probability theory and Bayesian networks. The techniques developed for quantitative and qualitative assessment are presented and compared in terms of their objectives, scopes, and results to pick out advantages and limits. Finally, the approaches dedicated to assessing the risks of web systems are presented.