Cybersecurity Systems Research Articles

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

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

Implementasi Nilai-Nilai Kebangsaan Berbasis UUD 1945 Guna Mendukung Sistem Cyber Security Dalam Penggunaan Sistem Informasi Akademik (SIA) Di Institusi Perguruan Tinggi

Cyber Security for Higher Education Academic Information System (AIS) data is an increasingly important issue along with the increasing dependence of higher education on information technology. Students have their education data in the Academic Information System (AIS) which is owned by each campus. Campuses are obliged to give their students the right to privacy and the right to information. The right to privacy and limitations on the right to information include state protection of personal data. In this way, students have the right to receive protection for their personal data in the academic information system (AIS). This is regulated by the 1945 Constitution (UUD 1945) and the Personal Data Protection Law (UU PDP), which are an important foundation for regulations on data management and cyber security systems. This research uses a journal literature review method which aims to investigate the security threats faced by Academic Information System (AIS) data and to design effective protection strategies. The methods used include risk analysis, identification of system vulnerabilities, and development of appropriate security policies. The findings show that sensitive data such as student personal information and academic records are the main targets for attackers. Therefore, the need to implement security measures such as data encryption, use of double authentication, strict system monitoring, as well as training for information system users is very important. Through a policy analysis approach and literature study, this research aims to identify strategies and best practices in integrating national values into cyber security policies and practices. It is hoped that this research will provide an overview of the importance of national principles in terms of cyber security in higher education. It will also provide recommendations for institutions to improve academic information systems in accordance with the country's constitution.

Analysis and Prevention of AI-Based Phishing Email Attacks

Phishing email attacks are among the most common and most harmful cybersecurity attacks. With the emergence of generative AI, phishing attacks can be based on emails generated automatically, making it more difficult to detect them. That is, instead of a single email format sent to a large number of recipients, generative AI can be used to send each potential victim a different email, making it more difficult for cybersecurity systems to identify the scam email before it reaches the recipient. Here, we describe a corpus of AI-generated phishing emails. We also use different machine learning tools to test the ability of automatic text analysis to identify AI-generated phishing emails. The results are encouraging, and show that machine learning tools can identify an AI-generated phishing email with high accuracy compared to regular emails or human-generated scam emails. By applying descriptive analytics, the specific differences between AI-generated emails and manually crafted scam emails are profiled and show that AI-generated emails are different in their style from human-generated phishing email scams. Therefore, automatic identification tools can be used as a warning for the user. The paper also describes the corpus of AI-generated phishing emails that are made open to the public and can be used for consequent studies. While the ability of machine learning to detect AI-generated phishing emails is encouraging, AI-generated phishing emails are different from regular phishing emails, and therefore, it is important to train machine learning systems also with AI-generated emails in order to repel future phishing attacks that are powered by generative AI.

The Legal Status of Digital Service Providers in the National Cybersecurity System

The Legal Status of Digital Service Providers in the National Cybersecurity System

A Review on Detection of Cybersecurity Threats in Banking Sectors Using Ai Based Risk Assessment

The continued rise of cybercrime in the current era has frightened not only off-users but also violating financial institutions. The recent studies have shown that most financial institutions lose the credibility of their clients and their investments as a result of these attacks. The cyber-hackers involved in these types of cybercrime are now using more advanced technology. Its vigour surpasses the firewall system currently in use our network. Hence, the security needs for financial institutions are increased. So, the server management and cyber security systems getting higher end security. The cyber security aspects are is always shifting based on the attacks, but the cyber security threats are increased and create the serious discussion about the financial institutions. In General, the cyberspace describes the combination of all types of communication networks, databases, embedded processors, Internet and controllers for exchanging electronic documents. The global network environment is created by telephone wires, coaxial cables, electromagnetic waves and fiber-optic lines. Although the terms cyberspace and the Internet can be used interchangeably, it is still part of cyberspace. In simple terms, cyberspace is a connected web environment. Now, cyber-security is the process of protecting cyberspace from attack, misuse, damage and economic espionage. Cyberspace can sometimes be blocked by intrinsic vulnerabilities that cannot be eliminated. In this study, different types of cyber cards are explained in detail. And the escape processes from certain subtle attack methods are clearly stated.

A Novel Architecture for an Intrusion Detection System Utilizing Cross-Check Filters for In-Vehicle Networks.

The Controller Area Network (CAN), widely used for vehicular communication, is vulnerable to multiple types of cyber-threats. Attackers can inject malicious messages into the CAN bus through various channels, including wireless methods, entertainment systems, and on-board diagnostic ports. Therefore, it is crucial to develop a reliable intrusion detection system (IDS) capable of effectively distinguishing between legitimate and malicious CAN messages. In this paper, we propose a novel IDS architecture aimed at enhancing the cybersecurity of CAN bus systems in vehicles. Various machine learning (ML) models have been widely used to address similar problems; however, although existing ML-based IDS are computationally efficient, they suffer from suboptimal detection performance. To mitigate this shortcoming, our architecture incorporates specially designed rule-based filters that cross-check outputs from the traditional ML-based IDS. These filters scrutinize message ID and payload data to precisely capture the unique characteristics of three distinct types of cyberattacks: DoS attacks, spoofing attacks, and fuzzy attacks. Experimental evidence demonstrates that the proposed architecture leads to a significant improvement in detection performance across all utilized ML models. Specifically, all ML-based IDS achieved an accuracy exceeding 99% for every type of attack. This achievement highlights the robustness and effectiveness of our proposed solution in detecting potential threats.

Enhanced MNB Method for SPAM E-mail/SMS Text Detection Using TF-IDF Vectorizer

Spam, whether in the form of SMS or email, poses significant threats by compromising user privacy and potentially leading to unauthorized access to personal data. In the era of smartphones, where users store sensitive information, the risk of cyber-attack through spam messages is heightened. This research addresses the pressing issue of spam SMS and email detection using a dataset comprising 5574 messages from reputable sources. The collection includes contributions from the National University of Singapore SMS Corpus, Grumble text Website, Caroline Tag’s PhD Theses, and SMS Spam Corpus v.0.1 Big. With a meticulous approach encompassing data cleaning, balancing, preprocessing, and exploratory data analysis, the research employs the TF-IDF (Term Frequency and Inverse Document Frequency) vectorizer to enhance the model’s ability to capture the importance of individual words. This foundational work sets the stage for evaluating various machine learning models, including Support Vector Machine, Multinomial Naïve Bayes, Decision Tree, Logistic Regression, Random Forest, AdaBoost, K-Nearest Neighbors, XGBoost, Gradient Boost Classifier, Bagging Classifier, and Extra Tree Classifier. Notably, the Multinomial Naïve Bayes model emerges as a standout performer with 100% accuracy and 97% precision in phishing detection. The research introduces an intuitive user interface, facilitating real-time interactivity for model assessment and offering valuable insights for cybersecurity applications. The study contributes to the advancement of robust cybersecurity systems, emphasizing precision and accuracy in spam SMS and email text detection.

Digital Era and Co-convergent Perspectives of International School Teachers in Beijing China

This research aimed to compare the perspectives on being teachers in the digital era for international schools in Beijing China between Chinese and foreign teachers. Data from samples, with a 5- rating-scale questionnaire, were analyzed by descriptive statistics. The finding pointed out that both Chinese and foreign international schoolteachers in the digital era had no statistically significant difference at the 0.05 level about having twelve co-convergent perspectives at the critical level as the baseline: knowledge, integrity, flexibility, expertise, management, professionalism, service mind, core competency, teaching, teamwork, communication, and problem-solving. Moreover, the more they develop knowledge, the more they add integrity for academic and cyber security systems before the other skills.

International Experience in Ensuring Cybersecurity in Local Self Government Management as a Component of the National Security of Ukraine

The study examines the critical relationship between cyber security as a component of national security and its provision in local government administration. The purpose of the study is to evaluate the international experience of ensuring cyber security in the management of local self-governments in Germany, Poland and Ukraine. In research were used visual and graphic methods, a set of methods of processing, comprehension and interpretation of information. This highlights the level of trust in local authorities in Poland, Germany and Ukraine, as well as a comparison of the systems of local self-government entities in the selected countries. It was established that there is a need to create and develop an adequate cybersecurity system. The result of the study is an analysis of the cyber security strategies of Germany, Poland and Ukraine. Prospects for further research are the analysis of the possibility of consolidation and differentiation of powers by subjects of local self-government in ensuring cyber security. An analysis of the real need for the powers related to providing cyber security and the ability to perform them is appropriate.

CySecBERT : A Domain-Adapted Language Model for the Cybersecurity Domain

The field of cysec is evolving fast. Security professionals are in need of intelligence on past, current and —ideally — upcoming threats, because attacks are becoming more advanced and are increasingly targeting larger and more complex systems. Since the processing and analysis of such large amounts of information cannot be addressed manually, cysec experts rely on machine learning techniques. In the textual domain, pre-trained language models such as Bidirectional Encoder Representations from Transformers (BERT) have proven to be helpful as they provide a good baseline for further fine-tuning. However, due to the domain-knowledge and the many technical terms in cysec, general language models might miss the gist of textual information. For this reason, we create a high-quality dataset 1 and present a language model 2 specifically tailored to the cysec domain that can serve as a basic building block for cybersecurity systems. The model is compared on 15 tasks: Domain-dependent extrinsic tasks for measuring the performance on specific problems, intrinsic tasks for measuring the performance of the internal representations of the model, as well as general tasks from the SuperGLUE benchmark. The results of the intrinsic tasks show that our model improves the internal representation space of domain words compared with the other models. The extrinsic, domain-dependent tasks, consisting of sequence tagging and classification, show that the model performs best in cybersecurity scenarios. In addition, we pay special attention to the choice of hyperparameters against catastrophic forgetting, as pre-trained models tend to forget the original knowledge during further training.

Cybersecurity of Industrial Systems—A 2023 Report

The article explores the importance of cybersecurity in Industry 4.0, specifically focusing on safeguarding industrial automation systems from cyberattacks. It discusses essential security measures, network monitoring, and employee training, emphasizing the significance of risk management for ensuring industry stability. Adherence to industrial security standards, such as ISA/IEC 62443, is crucial (ISA—International Society of Automation, IEC—International Electrotechnical Commission). The article outlines cyberthreat challenges and their impact on various sectors, including healthcare and finance, as well as the risks faced by large industrial enterprises. Additionally, it elucidates evolving cybersecurity strategies and principles, underscoring the necessity for continuous, multi-layered protection. Collaboration with operators, strict information security policies, and robust incident response plans are emphasized. The importance of risk monitoring and adaptability to ever-changing threat landscapes is highlighted, emphasizing the collaborative and flexible nature of cybersecurity in the face of escalating digital threats.

Правові аспекти забезпечення кібербезпеки в Україні: сучасні виклики та роль національного законодавства

The article identifies the peculiarities of the current state of cybersecurity in Ukraine and emphasizes the importance of applying effective legislation aimed at protecting the cyber structure, as well as choosing the best tools and mechanisms to combat cyber threats. For the purpose of this study, the author has selected the national legislation aimed at regulating the cybersecurity sector. The key aspects of legislation affecting cybersecurity and protection of information resources of Ukraine, in particular in the context of current challenges and threats of modern cyberspace, are quite effective today, as evidenced by the results of statistics on solving problems related to hacker attacks on Ukraine during the military conflict in 2022–2024. However, despite the fact that the current legislative acts create a solid basis for the development of Ukraine’s cybersecurity system, the article discusses issues that remain unresolved. In particular, the role of artificial intelligence in the field of data protection against cyberattacks in the current environment. The role of international cooperation in ensuring cybersecurity is also considered as one of the necessary conditions for ensuring the national security protection system of Ukraine.

Review of Machine learning: Views, Architectures or Techniques, Challenges and Future guidance and Real-world applications

In this digital world, the data is wealth, this data is analyzed, developed and applied to specific applications using some well-developed algorithms known as Machine learning (ML). Machine learning algorithms are supervised, unsupervised, semi-supervised and reinforcement types. Deep learning (DL) is also a method of analyzing the data on a large scale. Deep learning is a further subdivision of The subsection of ML is deep learning and measures a particular type of learning that involves the use of artificial neural networks (ANN). This paper provides group of different machine learning terminologies for quick reference. This study is important to focus on different machine learning techniques and their connection in various real-world applications such as smart cities, cyber security, healthcare, agriculture and intelligent transportation systems. In this paper, machine learning concepts, different types of architectures, the challenges, various real world applications are discussed.

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."

Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework

This research presents a comprehensive framework to enhance the cybersecurity and efficiency of smart urban energy systems by integrating cutting-edge technologies. Leveraging advanced evolutionary optimization techniques, specifically Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the proposed system optimizes energy management and trading within urban infrastructure. Simultaneously, the integration of Internet of Things (IoT) devices enables real-time monitoring and decision-making, while blockchain technology ensures secure and transparent transactions. The study evaluates system performance by comparing it with traditional approaches, assessing key metrics such as convergence speed, exploration, exploitation, and cybersecurity. Results indicate that the proposed framework outperforms traditional systems in energy efficiency, cost-effectiveness, and cybersecurity metrics. This research offers valuable insights into integrating emerging technologies to address evolving challenges in smart urban energy systems. The findings highlight the potential of this innovative framework to revolutionize urban energy management, providing a foundation for more resilient, secure, and efficient smart cities. As the global urban landscape continues to evolve, this framework represents a strategic approach to meet the increasing demands for sustainable and secure urban energy solutions.

Closing Disparities in Intrusion Detection: An In-Depth Analysis of IDPS Systems

Intrusion detection systems (IDS) are an important security tool that protects networks by monitoring policy breaches and possible threat activities Over the years, IDS technology has advanced significantly to combat cybercrime a it goes forward. The purpose of this paper is to provide a comprehensive review of IDS technology, addressing implementation challenges, research limitations, and future directions. By exploring the various research sub-categories in IDS, readers will gain insight into industry needs and the importance of ongoing research efforts Keywords: Intrusion Detection System (IDS), Threat Detection, Attackers, Network Security, Cybersecurity Systems AIMS-MATHS JOURNAL REFERENCE FORMAT Olasegi, I.G. (2024 Closing Disparities in Intrusion Detection: An In-Depth Analysis of IDPS Systems. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 100-108. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P8

Optimizing E-Government Cybersecurity through Artificial Intelligence Integration

This study explores the integration of Artificial Intelligence (AI) into e-governance cybersecurity, focusing on its key insights, contributions, challenges, and future directions. AI-powered cybersecurity systems offer advanced capabilities in threat detection, response, and scalability, improving the protection of critical infrastructure and sensitive data in government digital services. However, ethical considerations such as transparency, fairness, and accountability, as well as challenges related to algorithm biases, data privacy, and cybersecurity skills gap, must be addressed to ensure responsible and effective use of AI technologies. Recommendations for policymakers, government agencies, and researchers are provided to maximize the benefits of AI while mitigating potential risks and enhancing e-governance cybersecurity resilience in the digital age.

Detecting Anomalies and Intrusions in Unstructured Cybersecurity Data Using Natural Language Processing

Abstract: Due to the growing volume and variety in data generated by cybersecurity systems, it is crucial that unstructured text be used for detecting anomalies. Natural language processing is a powerful tool for analyzing unstructured information and identifying threats. This paper presents a comprehensive review of NLP applications for cybersecurity. We first present the motivations for and challenges associated with using NLP to improve cybersecurity. Then, we provide background information on unstructured data that is relevant to cybersecurity, and discuss NLP techniques such as named entity recognition (NEAR), sentiment analysis, topic modelling, and document classifying. This paper focuses on how these techniques are used to detect anomalies and intrusions. We present a taxonomy for NLP-driven approaches, and we conduct a literature review that is categorized according to this taxonomy. We examine critically the strengths and weaknesses of current techniques. We highlight research gaps based on this analysis and propose a research agenda to advance NLP research in cybersecurity applications. This paper summarizes previous research and lays the foundation for using NLP to tackle cybersecurity challenges that involve unstructured data.

Cyber5Gym: An Integrated Framework for 5G Cybersecurity Training

The rapid evolution of 5G technology, while offering substantial benefits, concurrently presents complex cybersecurity challenges. Current cybersecurity systems often fall short in addressing challenges such as the lack of realism of the 5G network, the limited scope of attack scenarios, the absence of countermeasures, the lack of reproducible, and open-sourced cybersecurity training environments. Addressing these challenges necessitates innovative cybersecurity training systems, referred to as “cyber ranges”. In response to filling these gaps, we propose the Cyber5Gym, an integrated cyber range that enhances the automation of virtualized cybersecurity training in 5G networks with cloud-based deployment. Our framework leverages open-source tools (i) Open5GS and UERANSIM for realistic emulation of 5G networks, (ii) Docker for efficient virtualization of the training infrastructure, (iii) 5Greply for emulating attack scenarios, and (iv) Shell scripts for automating complex training operations. This integration facilitates a dynamic learning environment where cybersecurity professionals can engage in real-time attack and countermeasure exercises, thus significantly improving their readiness against 5G-specific cyber threats. We evaluated it by deploying our framework on Naver Cloud with 20 trainees, each accessing an emulated 5G network and managing 100 user equipments (UEs), emulating three distinct attack scenarios (SMC-Reply, DoS, and DDoS attacks), and exercising countermeasures, to demonstrate the cybersecurity training. We assessed the effectiveness of our framework through specific metrics such as successfully establishing the 5G network for all trainees, accurate execution of attack scenarios, and their countermeasure implementation via centralized control of the master using automated shell scripts. The open-source foundation of our framework ensures replicability and adaptability, addressing a critical gap in current cybersecurity training methodologies and contributing significantly to the resilience and security of 5G infrastructures.

Effect of Adopting AI to Explore Big Data on Personally Identifiable Information (PII) for Financial and Economic Data Transformation

The integration of Artificial Intelligence (AI) into big data analytics represents a pivotal shift in the management of Personally Identifiable Information (PII) within the financial sector. This study was prompted by the increasing reliance on AI for handling sensitive financial data and the consequent rise in data security concerns, exemplified by the 2019 Capital One data breach which compromised the PII of over 100 million individuals, highlighting the vulnerabilities inherent in digital data storage and management systems. Aiming to critically evaluate the effects of adopting AI in exploring big data on PII within the financial and economic sectors, the study focused on assessing how AI can transform data management processes, enhance data security, ensure compliance with regulatory requirements, and maintain data integrity. Employing a quantitative research methodology, data was gathered from 532 professionals in the financial sector through surveys distributed via LinkedIn. The hypotheses were tested using multiple regression analysis. The study's findings revealed that the adoption of AI in managing big data significantly enhances the security and privacy of PII in the financial sector. However, it also increases the risk of sophisticated cyber-attacks such as adversarial attacks and data poisoning. Significantly, financial institutions that integrate AI into their data management systems demonstrate higher compliance with data protection regulations, and AI-driven cybersecurity strategies were found to markedly improve the performance of cybersecurity systems in the sector. Based on these insights, the study recommends best practices and guidelines for financial institutions to effectively integrate AI into their data management systems. These include prioritizing data security and privacy, ensuring regulatory compliance, investing in AI-driven cybersecurity, and managing the inherent risks of AI integration. The study advocates for a balanced approach in AI adoption, emphasizing the need for robust security measures, continuous monitoring, and adapting to the evolving regulatory and technological landscape.