Cybersecurity Systems Research Articles

Model‐Based Systems Engineering (MBSE) Application in Nuclear Power Plants (NPP)

AbstractCompanies in the nuclear power sector are constantly being challenged to improve their safety and reliability due to increasing complexity arise from evolving safety regulations, long production life, interdisciplinary collaboration, and the need for analyzing the impact of the changes in an operational life cycle. Recognizing these challenges, the paper proposes a transition to Model‐Based Systems Engineering (MBSE) as a transformative solution to improve the management of such complex systems. With this objective, this paper presents a workflow implementation that demonstrates the MBSE methodologies to define a concept model, system architecture, impact analysis, safety and reliability analysis, and operational decision‐making of Nuclear Power Plants (NPP). The paper concludes that MBSE provides a potent approach to managing NPP by employing graphical models to develop interrelated systems that has strong adaptability to heterogeneous environments and regulatory changes. The simulation results demonstrated an NPP life cycle, impact analysis, and a test case for model‐based safety and reliability analysis for regulatory compliance, operational efficiency, balance safety, and informed decision‐making in NPP. The study also leads to a number of interesting directions of future work such as synchronization through Product Lifecycle Management, integration with Building Information Modeling, Model‐Based Commissioning/Decommissioning, and Model‐Based Cyber System Security tailored for nuclear power systems.

A comparison of several intrusion detection methods using the NSL-KDD dataset

The increasing significance of cybersecurity underscores the critical necessity of addressing evolving methods of hackers. This research investigates the way to classify and predict cyber-attacks on the NSL-KDD dataset using intrusion detection methods the investigation contrasts the capabilities of various algorithms, including RNN, MLP, CNN-LSTM, and ANN, in recognizing attacks. The results indicate that both MLP and RNN have the greatest efficiency and effectiveness for different time frames. these findings demonstrate the necessity of Constant evaluation and enhancement of intrusion detection systems in order to remain aware of the dynamic nature of the cyber threat landscape. Addressing cybersecurity issues necessitates a comprehensive approach that combines computational enhancements, human talent, organizational policies, and regulatory frameworks in order to create a powerful and stable cybersecurity system.

Қаржы-экономикалық кеңістікке негізделген бухгалтерлік есептің болашағы: бизнес сектордегі ақпараттық жүйесі

The article discusses future of accounting based on financial and economic space, information systems in the business sector are synonymous with cloud accounting. Its most important elements are cybersecurity system, smart manufacturing, Internet of broadcasting, Big Data and others. The proliferation of digital technologies has not affected many aspects modern civilization. Is rethought professional structures and reinvented business models. Consequently, in conditions IS, financial and accounting services (AS) face new threats, difficulties and opportunities. In business, players AS sector accept that traditional accounting system is gradually being replaced by digital AS. The purpose to answer this question based on qualitative and research approach applied to research using semi - structured interviews. The study while digital transformation in Kazakhstan's business sectors providing accounting services is just beginning, respondents highlighted cloud information system, Optical Character Recognition (OCR), artificial intelligence (AI), robotics and enterprise resource planning (ERP). Resistance to change, organizational culture and pricing seem be main obstacles for IS in accounting based on the financial and economic space. In the context of digital age, organizations contribute to a more complete understanding of accounting and role accountants. In addition, it provides practical insight into possible relationship between technological (in particular, digital) development and dynamics labor market for accounting professionals. Finally main conclusion and limitations this study described in final section, addition possibilities future research on topic.

Investigation of associative rule search method for detection of cyber incidents in information management systems and security events using CICIDS2018 test data set

Automated rule generation for cyber incident identification in information management and security event systems (SIEM, SYSTEM, etc.) plays a crucial role in modern cyberspace defense, where data volumes are exponentially increasing, and the complexity and speed of cyber-attacks are constantly rising. This article explores approaches and methods for automating the process of cyber incident identification rule generation to reduce the need for manual work and ensure flexibility in adapting to changes in threat models. The research highlights the need for utilizing modern techniques of Intelligent Data Analysis (IDA) to process large volumes of data and formulate behavior rules for systems and activities in information systems. The conclusion emphasizes the necessity of integrating multiple research directions, including analyzing existing methods and applying IDA algorithms to search for associative rules from large datasets. Key challenges addressed include the complexity of data modeling, the need to adapt to changes in data from dynamic cyber attack landscapes, and the speed of rule generation algorithms for their identification. The issue of the "dimensionality curse" and the identification of cybersecurity event sequences over time, particularly relevant to SIEM, are discussed. The research objective is defined as the analysis and evaluation of various mathematical methods for automated associative rule generation to identify cyber incidents in SIEM. The most effective strategies for enhancing the efficiency of associative rule generation and their adaptation to the dynamic change of the cybersecurity system state are identified to strengthen the protection of information infrastructure.

Cognitivism as the Basis of Artificial Intelligence

The article examines the main issues of cognitivism as the basis of artificial intelligence (AI) in a modern philosophical interpretation of these entities. A classification of AI is given according to the level of cognitivism of basic functions. We consider the issues of the evolution of the cognitive capabilities of artificial intelligence. The problems of predictability of the negative impact of AI on society are raised. The article highlights the main cognitive distortions that are possible when using artificial intelligence in research, namely, the illusion of research breadth. The authors provide recommendations for researchers and editors of academic journals regarding a competent use of AI in scientific experiments. This work also raises the issue of trust in the field of cybersecurity of AI systems. The authors consider the hypothesis about the presence of consciousness in chatbots and draw clear conclusions about its absence.

Analisis Resiko Operasional dalam Industri Travel Yuk Kita Tour Untuk Meningkatkan Efisiensi dan Kepuasan Pelanggan

This study aims to investigate the identification, assessment, and control of operational risks in the travel industry, with a focus on PT Yuk Kita Tour. Direct observation and interview methods are used to identify the risks faced by the company. Risk assessment is conducted by considering the likelihood of occurrence and its impact on the company's operations, followed by appropriate mitigation strategies. The analysis results show that risks such as online booking system disruptions, lack of staff training, currency exchange rate fluctuations, regulatory changes, natural disasters, disease outbreaks, transportation delays, and terrorism threats affect operational efficiency and customer satisfaction.To control these risks, PT Yuk Kita Tour implements a comprehensive mitigation strategy, including improved cybersecurity systems, staff training programs, currency hedging strategies, active regulatory monitoring, travel insurance, strict health policies, cooperation with transportation operators, and clear security information to customers. The implementation of these risk control strategies has had a positive impact on the company's performance, with year-on-year increases in revenue and customer satisfaction. This study provides insights into the importance of operational risk management in the travel industry and demonstrates that a proactive approach to risk identification, assessment, and control is possible. This study provides insight into the importance of operational risk management in the travel industry and demonstrates that a proactive approach to risk identification, assessment, and control can improve operational efficiency and customer satisfaction. The practical implication of this study is the importance of continuously monitoring existing risks, adapting control strategies, and involving stakeholders in risk management to ensure sustainable business continuity and superior customer service in a dynamic business environment.

Proactive cyber fraud response: a comprehensive framework from detection to mitigation in banks

PurposeThis study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.Design/methodology/approachThe methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.FindingsFindings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.Practical implicationsThe practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.Originality/valueThe originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

Security Ontology in a Virtual University

Abstract In this paper we propose a cybersecurity ontology model designed for universities, aiming to facilitate the management and protection of sensitive data and information within the context of the growing cybersecurity threats. The proposed ontology includes four distinct hierarchical levels: the basic level, the conceptual level, the instance level and the relationships level. At the basic level, it defines essential terms and principles of cybersecurity, including concepts like vulnerability, threat, cyber-attack, security policies and security rules. At the conceptual level, the ontology categorizes information and cybersecurity systems, embracing domains such as data protection, authentication, authorization, and auditing. At the instance level, the ontology describes specific examples of information and cybersecurity systems used in universities, such as the library management system or the accounting management system. At the relationships level, the ontology establishes links between different categories of information and cybersecurity systems, as well as between these systems and the entities that use them, such as students, professors and administrative staff. By implementing this cybersecurity ontology, universities can improve the management and protection of their sensitive data and information, as well as respond more efficiently to cybersecurity threats.

Entropy based network anomaly detection

an era of relentless cyber threats, the increasing complexity and volume of data significantly intensify the risk and impact of cybersecurity breaches. As organizations generate and store more data, the potential attack surface grows, providing more opportunities for malicious actors to exploit vulnerabilities. Consequently, there is a growing necessity for more advanced analytical techniques to effectively detect and mitigate these evolving threats. Shannon entropy, introduced by Claude Shannon in 1948, is a fundamental concept in information theory that measures the unpredictability or randomness of information. It serves as a primary tool for identifying unusual patterns within extensive datasets, offering a quantitative approach to detect anomalies This paper explores the application of Shannon’s Entropy to detect and prevent distributed denial-of-service (DDoS) attacks. Unlike traditional motif identification tools, which focus on recurring patterns within data, Shannon entropy provides a broader measure of randomness and can detect subtle variations that may indicate a security breach. By leveraging the entropy measure, cybersecurity systems can identify and respond to abnormal traffic patterns that signify a potential DDoS attack, thereby enhancing the robustness and reliability of data protection mechanisms

Power Network Vulnerability Assessment Based on Electric Power System Cyber Security Simulation Platform

Abstract This paper introduces a modeling approach for the integration of electric power and information networks, along with the creation of an electric power system network simulation platform. The system, achieved through the deep integration of computation, communication, and control technologies, offers new insights into power grid intelligence. The paper discusses the concept of power system vulnerability and presents a modeling scheme that effectively simulates and analyzes power systems. Using the Analytic Hierarchy Process (AHP), the impact of cyber attacks under different load conditions is evaluated, revealing that attacks have more severe consequences under heavy loads. In conclusion, this research contributes to enhancing the security and stability of power systems.

Intrusion Detection System Using Probabilistic Adaptive Learning

Abstract: Machine learning and deep learning techniques are widely used to evaluate intrusion detection systems (IDS) capable of rapidly and automatically recognizing and classifying cyber-attacks on networks and hosts. However, when destructive attacks are becoming more extensive, more challenges develop, needing a comprehensive response. Numerous intrusion detection datasets are publicly accessible for further analysis by the cybersecurity research community. The Intrusion Detection System (IDS) is an effective tool utilized in cybersecurity systems to detect and identify intrusion attacks. With the increasing volume of data generation, the possibility of various forms of intrusion attacks also increases. Feature selection is crucial and often necessary enhance performance. The structure of the dataset can impact the efficiency of the machine learning model. Furthermore, data imbalance can pose a problem, but sampling approaches can help mitigate it. With technological advancements, machine learning-based methods have emerged as the cornerstone of modern intrusion detection, enabling more precise identification of abnormal behaviors and potential intrusions by learning the patterns of normal network traffic. In response to these challenges, this paper introduces an innovative intrusion detection model that amalgamates the Probabilistic Adaptiv eLearning Network architecture.

Data Science in Cybersecurity to Detect Malware-Based Domain Generation Algorithm: Improvement, Challenges, and Prospects

Nowadays, the malware communicates with command and control servers using domains generated algorithmically. Domain generation algorithms ( DGAs) are continually evolving, which degrades the accuracy of the existing methods calls for the continuous tracking of how DGAs develop and their detection methods and calls for a good evaluation of the stage to open horizons for new detection methods. Data science plays a key role in cybersecurity by providing methods for detecting and analyzing network traffic data, including DGAs, and helping to improve the overall security of computer systems and networks. It can also be used to analyze large datasets of domain names and to develop and optimize solutions for DGA detection, by applying techniques such as machine learning, deep learning, and genetic algorithms, which have shown their effectiveness in detecting new and unknown DGAs. This paper reviews the role of data science in cybersecurity systems to detect DGAs. Hence, it also brings together publicly available domain name datasets and data science techniques utilized in recent DGA detection systems to highlight current issues and potential directions. This article additionally explains issues related to DGA detection. This will assist researchers in improving the current DGA detection algorithms as well as creating new powerful models.

Understanding the role of artificial intelligence in enhancing GRC practices in cybersecurity

In cybersecurity, the integrity and security of data and systems can be preserved with governance, risk, and compliance (GRC) practices. As the complexity of cyber threats increases, organizations need to enhance their GRC practices with advanced technologies. This research article studies the role of artificial intelligence in reinforcing GRC practices in cybersecurity. The study provides a comprehensive GRC overview in the context of cybersecurity, highlighting its importance in maintaining a secure and compliant environment. It then examines how AI can enhance GRC practices, including analyzing data, automating compliance processes, and identifying potential threats. Furthermore, the paper discusses implementing AI-driven GRC solutions, outlining key organizational considerations and best practices. It also addresses ethical and regulatory considerations surrounding AI in GRC in decision-making processes. This study highlights insights into how AI can effectively strengthen cybersecurity GRC practices, ultimately helping governments and organizations mitigate risks and enhance their cybersecurity posture in an increasingly digital world.

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."

Cyber Security Systems in Manufacturing Process Application: A Review

In recent times, technological progress has become increasingly pivotal across various sectors, particularly in engineering and its associated fields. However, organizations are grappling with numerous challenges, foremost among them being security threats such as unauthorized access to sensitive company data by external entities. The prevalence of such security issues has emerged as a significant concern for many enterprises, as the economic stability of an organization hinges on the secure integration of diverse information assets, free from external vulnerabilities. This study delves into existing literature on advancements in cybersecurity within manufacturing process networks, aiming to bolster safety measures against cyber-attacks and fortify operations. With the advent of Industry 4.0, there is a ripe opportunity to infuse modernizing elements into manufacturing systems, thereby fortifying their security infrastructure. By leveraging cybersecurity protocols, organizations can safeguard information integrity to uphold stringent safety standards effectively.

THE SIGNIFICANCE OF LEGAL REGULATION IN ENSURING CYBER SECURITY IN THE CONDITIONS OF WAR: EXPERIENCE FOR UKRAINE

Introduction. In the vast majority of countries, there is a constant trend towards a significant increase in the number and expansion of the spectrum of cyber-attacks with the aim of violating the confidentiality, integrity and availability of state information resources, in particular those distributed on the Internet and objects of critical information infrastructure. Summary of main results. The large-scale deployment of a hybrid war against our state seriously affects all aspects of social life. To a large extent, the strategic success of countering the challenges of hybrid warfare depends on the effective and quality of the legal response. Ensuring an adequate level of cyber security is difficult to imagine without clearly planned joint actions and measures developed by responsible entities, which must be synchronized and implemented according to a single strategic plan and development vector of the national cyber security system of a declarative nature. That is why cyber security in most countries of the world is recognized as an important component of national security, the provision of which is impossible without the formation and functioning of a national system in the field of cyber security, which is based on such principles as respect for the principles and norms of international law, protection of fundamental values defined by current legislation, protection national interests in cyberspace. It is emphasized that the state's national security largely depends on the state of cyber security. It has been proven that hybrid war significantly increases the impact of cyber threats on Ukrainian society and actualizes the danger of targeted cyber attacks as a tool of aggression against our state in the context of global trends regarding threats in cyberspace. Conclusions. In the process of conducting the research, we formulated conclusions in which special attention was paid to the peculiarities of the current state of functioning of the legislation on cyber security and considered promising directions for its improvement in the future, which, in turn, will become the basis for improving the administrative and legal regulation of cyber security in Ukraine.

Analysis of the Impact of Cybersecurity on The Stability of Financial Institutions

The issue of the impact of cybersecurity is a top priority for the functioning of a financial institution. Therefore, its provision becomes a priority in organizing the further activities of the institution itself. The article aims to analyze the current trends in ensuring the cybersecurity of financial institutions and its role in their functioning. The paper describes the theoretical foundations of the cyber protection concept. The authors also outline the specifics of cybersecurity and the leading technological solutions for achieving these goals. Special attention is paid to modern, innovative solutions to maintain the stability of financial institutions and the possibility of further development. An important area of research is the disclosure of the practical experience of information corporations in providing technical solutions to ensure the security of financial transactions and support the global financial system. The study presents the experience of Ukrainian financial institutions that use variable digital defense strategies and maintain their functionality based on an effective cybersecurity system in the context of a global military conflict. The results of the study include a detailed profile of effective practical solutions to ensure the stability of financial institutions based on the implementation of blockchain technologies. The authors pay attention to the issue of modern software, hardware, and management solutions to improve the quality of cybersecurity in the context of growing cyberattacks and digital threats to the stability of financial institutions. The research findings may be helpful for further analytical studies on the effectiveness of cybersecurity and the stability of financial institutions.

Preserving manipulated and synthetic Deepfake detection through face texture naturalness

With the rapid development of deep learning and face recognition technology, AI(Artificial Intelligence) experts have rated Deepfake cheating as the top AI threat. It is difficult for the human eye to distinguish the fake face images generated by Deepfake. Therefore, it has become a popular tool for criminals to seek benefits. Deepfake can be mainly divided into two types, a manipulated Deepfake that falsifies images of others by targeting real faces, and a synthetic Deepfake using GAN to generate a new fake image. So far, seldom cybersecurity system is able to detect these two types simultaneously. In this article, we aim to propose a hybrid Deepfake detection mechanism (HDDM) based on face texture and naturalness degree. HDDM constructs a unique texture from a facial image based on CNN(Convolutional Neural Network) and builds a naturalness degree recognition model via DNN(Deep Neural Network) to help cheating detection. Experimental results have proved that HDDM possesses a sound effect and stability for synthetic and manipulated Deepfake attacks. In particular, the WildDeepfake simulation has demonstrated the possibility of applying HDDM to the real world.

The Legal Situation of Operators of Essential Services and Digital Service Providers in the Provisions of the Act of 5 July 2018 on the National Cybersecurity System

Abstract The Act of 5 July 2018 on the National Cybersecurity System and its accompanying executive regulations have introduced into Polish law the provisions of the Directive of the European Parliament and of the Council of 6 July 2016 concerning measures for a high common level of security of network and information systems across the Union (UE) 2016/1148. The fundamental reason for these regulations was to establish a coherent system to ensure the cyber security of the Republic of Poland with accordance to standards adopted for European Union Member States. This paper presents the legal situation of operators of essential services and digital service providers that was created by the provisions of the ANCS. The ANCS not only identifies operators of essential services, digital service providers, and their assigned obligations, but also addresses the competent authorities’ tasks of supervising, inspecting and imposing penalties within the cyber security system. The findings, assessments and conclusions presented here are based on the interpretation of the provisions of the ANCS and are supported by prominent claims of academic representatives. The analyses contained within this paper aim to show that despite the comprehensible and contemporary ratio legis – which falls within the framework of pursuing the state of digital safety – the provisions of the ANCS require adjustments that acknowledge the legal situation of operators of essential services and digital service providers.

An approach to identify an approximating model of a cyber security system

An approach to identify an approximating model of a cyber security system