Cyber Security Intelligence Research Articles

Combining Cyber Security Intelligence to Refine Automotive Cyber Threats

Modern vehicles increasingly rely on electronics, software, and communication technologies (cyber space) to perform their driving task. Over-The-Air (OTA) connectivity further extends the cyber space by creating remote access entry points. Accordingly, the vehicle is exposed to security attacks that are able to impact road safety. A profound understanding of security attacks, vulnerabilities, and mitigations is necessary to protect vehicles against cyber threats. While automotive threat descriptions, such as in UN R155, are still abstract, this creates a risk that potential vulnerabilities are overlooked and the vehicle is not secured against them. So far, there is no common understanding of the relationship of automotive attacks, the concrete vulnerabilities they exploit, and security mechanisms that would protect the system against these attacks. In this article, we aim at closing this gap by creating a mapping between UN R155, Microsoft STRIDE classification, Common Attack Pattern Enumeration and Classification (CAPEC), and Common Weakness Enumeration (CWE). In this way, already existing detailed knowledge of attacks, vulnerabilities, and mitigations is combined and linked to the automotive domain. In practice, this refines the list of UN R155 threats and therefore supports vehicle manufacturers, suppliers, and approval authorities to meet and assess the requirements for vehicle development in terms of cybersecurity. Overall, 204 mappings between UN threats, STRIDE, CAPEC attack patterns, and CWE weaknesses were created. We validated these mappings by applying our Automotive Attack Database (AAD) that consists of 361 real-world attacks on vehicles. Furthermore, 25 additional attack patterns were defined based on automotive-related attacks.

Analysis of modern intelligent methods for protecting critical information infrastructure

Critical information infrastructure (CII), including the financial sector, plays a key role in ensuring the sustainable functioning of economic systems and the financial stability of States. However, the growing digitalization of the financial industry and the introduction of innovative technologies are opening up new attack vectors for attackers. Modern cyber attacks are becoming more sophisticated, and traditional defenses are proving ineffective against new, previously unknown threats. There is an urgent need for more flexible and intelligent cybersecurity systems. Thus, the subject of the study is modern intelligent methods and technologies for protecting critical information infrastructure (CII) from cyber attacks. The object of the research is methods and means of ensuring the protection of critical information infrastructure using artificial intelligence and machine learning technologies. The methodological basis of this study is a comprehensive analysis of the scientific literature on the use of intelligent methods and technologies to protect critical information infrastructure. During the review and critical analysis of relevant scientific publications, key problems and unresolved tasks requiring further scientific research and practical developments in this subject area were identified. This methodological approach allowed us to form a holistic view of the current state and prospects for the development of intelligent cybersecurity tools for critical financial systems, as well as to identify priority areas for further research. The main directions of scientific novelty of this research are: 1. A detailed review of promising approaches based on artificial intelligence and machine learning technologies to ensure effective protection of CII organizations from modern complex cyber attacks. 2. Identification and analysis of a number of key scientific and technical problems that need to be solved to increase reliability, interpretability and trust in intelligent cybersecurity systems, including issues of robustness to attacks, active online learning, federated and differential private data processing. 3. Identification of promising areas for further research and development in the field of application of specialized methods of secure and trusted AI to protect critical financial infrastructure. Thus, this research makes a significant contribution to the development of scientific and methodological apparatus and practical solutions for the use of intelligent methods to ensure cybersecurity.

The Role of Civilian Cybersecurity Companies in Military Cyber Operations

Abstract The Russia-Ukraine war has clearly shown that critical infrastructures are prime targets for cyber operations in addition to the physical domain. In practice, in many cases, these critical infrastructures are protected by civilian cybersecurity companies in the context of a managed security service, so their defense operations must necessarily be coordinated with military defense activities. This includes among others the sharing of information classified under different classification systems (national, EU, NATO) between different actors in national cyber defense, the inclusion of appropriate civilian experts in the armed forces, and the usage of cutting-edge cybersecurity technologies (e.g. AI-enabled solutions) that are first introduced for civilian use, with military organizations only having access to them later or possibly not encountering them at all due to procurement difficulties. The main goal of this paper is to introduce the existing opportunities and obstacles to civilians’ involvement in military cyber operations in the areas of legislation, technology, and human resources from the European perspective. Moreover, the paper deals with the actual questions of cybersecurity intelligence sharing between civilian and military entities and the European Union’s actions in order to improve the overall cybersecurity posture of the region.

The Role of Artificial Intelligence in Cybersecurity: Automation of Protection and Detection of Threats

The Role of Artificial Intelligence in Cybersecurity: Automation of Protection and Detection of Threats

Strengthening Internet of Things Security: Surveying Physical Unclonable Functions for Authentication, Communication Protocols, Challenges, and Applications

The spectrum of Internet of Things (IoT) applications is vast. It serves in various domains such as smart homes, intelligent buildings, health care, emergency response, and many more, reflecting the exponential market penetration of the IoT. Various security threats have been made to modern-day systems. Cyberattacks have seen a marked surge in frequency, particularly in recent times. The growing concern centers around the notable rise in cloning attacks, persisting as a significant and looming threat. In our work, an in-depth survey on the IoT that employs physically unclonable functions (PUFs) was conducted. The first contribution analyzes PUF-based authentication, communication protocols, and applications. It also tackles the eleven challenges faced by the research community, proposes solutions to these challenges, and highlights cloning attacks. The second contribution suggests the implementation of a framework model known as PUF3S-ML, specifically crafted for PUF authentication in the Internet of Things (IoT), incorporating innovative lightweight encryption techniques. It focuses on safeguarding smart IoT networks from cloning attacks. The key innovation framework comprises three stages of PUF authentication with IoT devices and an intelligent cybersecurity monitoring unit for IoT networks. In the methodology of this study, a survey relevant to the concerns was conducted. More data were provided previously regarding architecture, enabling technologies, and IoT challenges. After conducting an extensive survey of 125 papers, our analysis revealed 23 papers directly relevant to our domain. Furthermore, within this subset, we identified 11 studies specifically addressing the intersection of communication protocols with PUFs. These findings highlight the targeted relevance and potential contributions of the existing literature to our research focus.

Artificial intelligence in cybersecurity: Protecting national infrastructure: A USA review

Artificial Intelligence (AI) has emerged as a transformative force in the field of cybersecurity, playing a pivotal role in safeguarding national infrastructure. This review focuses on the application of AI technologies within the context of the United States, examining their efficacy in fortifying critical systems against evolving cyber threats. The paper delves into various AI-driven cybersecurity strategies, ranging from anomaly detection and predictive analysis to threat intelligence and automated response mechanisms. The integration of AI in cybersecurity not only enhances the speed and accuracy of threat detection but also addresses the dynamic nature of cyber threats. The specific AI technologies employed in the United States, including machine learning, natural language processing, and neural networks, highlighting their contributions to bolstering the resilience of national infrastructure are also examined. Furthermore, the challenges and ethical considerations associated with the widespread adoption of AI in cybersecurity are assessed. It discusses the need for robust regulatory frameworks to govern the deployment of AI in sensitive domains and emphasizes the importance of collaboration between government agencies, private enterprises, and research institutions to foster innovation and address emerging threats. In conclusion, this review provides a comprehensive analysis of the role of AI in cybersecurity within the United States, emphasizing its significance in protecting critical national infrastructure. By exploring technological advancements, challenges, and ethical considerations, this paper contributes to the ongoing discourse on leveraging AI to safeguard against the ever-evolving landscape of cyber threats.

The Future of Artificial Intelligence in Cyber Security A Review

The Future of Artificial Intelligence in Cyber Security A Review

The Weaponization of Artificial Intelligence in Cybersecurity: A Systematic Review

The weaponization of artificial intelligence (AI) and machine learning (ML) models in cybersecurity is a growing concern, with cybercriminal organizations and nation-states exploiting their weaknesses. The Microsoft "Tay" chatbot incident exemplifies the risks of weaponized AI, as it displayed sexist and racist behaviors due to malicious data inputs. The researcher examined 21 academic studies on AI weaponization and AI-driven cyberattacks in this systematic review. This work, a systematic review, concludes with an in-depth understanding of the scale and scope of using AI as a cyber weapon. The findings revealed that more research is necessary on weaponizing AI for offensive cybersecurity applications and the following key observations (a) a connection between AI weaponization and countermeasures, (b) AI’s role in enhancing cybersecurity defenses, (c) AI weaponization offering mitigation strategies for protecting digital assets and infrastructure, and (d) AI-driven attacks exploiting vulnerabilities, enabling automation and scalability, facilitating data poisoning and manipulation, improving social engineering, and augmenting evasion obfuscation. This study contributes to a better understanding of AI weaponization and aids researchers in synthesizing current literature on the topic.

Artificial Intelligence in Cyber Security

Artificial Intelligence in Cyber Security

Applications of artificial intelligence in cyber security

Applications of artificial intelligence in cyber security

The Role of Cognitive-Information Technologies in Cybersecurity: Threat Detection and Adaptive Defense Systems

The research delves into the influence of machine learning and artificial intelligence advancements on cybersecurity within software-oriented systems. The author thoroughly examines the modeling of cognitive-information technologies and their ramifications on data analysis, training processes, and decision-making within these systems. Special emphasis is placed on identifying cybersecurity threats faced by artificial intelligence systems, such as susceptibility to cyberattacks. The study proposes adaptive defense components, including behavioral biometrics analysis, automated incident response, user and entity behavior analytics (UEBA), and vulnerability management, to address these threats. These components are underscored in the development of cybersecurity strategies in the contemporary digital environment, crucial for protecting sensitive data and infrastructure. Methodologically, the research involves analyzing existing cybersecurity threats and their impact on artificial intelligence systems, employing data analytics and modeling techniques tailored to information technologies. It also evaluates contemporary methods of adaptive cybersecurity. Key findings of the study not only identify cybersecurity threats to artificial intelligence systems but also propose adaptive defense components for effective mitigation. The research innovatively examines the influence of cognitive information technologies on cybersecurity strategies, offering novel approaches to safeguard data and infrastructure in the modern digital landscape. Additionally, the study highlights examples such as Natural Language Processing (NLP), image and video recognition, predictive analytics, and virtual assistants, which are integral to understanding the breadth of applications of artificial intelligence in cybersecurity. The author significantly contributes through a systematic analysis of diverse threats, culminating in comprehensive recommendations for cybersecurity. Furthermore, the study identifies future prospects for cybersecurity amidst evolving cyber threats, paving the way for further research and development in the field and enhancing understanding and ensuring security in the digital realm.

Artificial Intelligence in Cybersecurity: A Socio-Technical Framing

Artificial Intelligence in Cybersecurity: A Socio-Technical Framing

Забезпечення кібербезпеки в еру штучного інтелекту: аналіз технологічних підходів та стратегій для захисту інформації

Technologies based on machine learning are gradually being introduced as a new means of automation of everyday and routine tasks, providing new opportunities for creativity and innovation. However, the growing use of artificial intelligence raises concerns about the security of personal data of users and the confidential developments of certain companies. When using this tool, it is important to be aware of the potential risks to information and cybersecurity. The purpose of the article was to analyze the issue of ensuring cybersecurity in the era of artificial intelligence and technological approaches and strategies for information protection. The Cybersecurity Strategy of Ukraine, which was approved by the Decree of the President of Ukraine dated August 26, 2021, was analyzed. Artificial intelligence has been identified as having both positive and negative effects in cybersecurity, as it can either enhance the cyber attack process, causing faster and more damaging attacks, or conversely, improve cybersecurity. The main threats of artificial intelligence for cybersecurity are vulnerability, privacy, attacks of various types. The spread of artificial intelligence creates a threat of disclosure of confidential information during data collection and processing, which requires the introduction of appropriate security measures at all stages of technology use. In general, the use of artificial intelligence requires not only innovative approaches, but also increased security measures to protect against various cyber threats. It is found that the capabilities of artificial intelligence can be used in cybersecurity, which can contribute to the strengthening of protection for organizations and reduce the workload of relevant specialists. The tools built on the basis of artificial intelligence allow you to automate routine tasks in the field of security, freeing up the time of experts to solve the most important tasks. In general, the potential of artificial intelligence can be used to strengthen cybersecurity, but it is necessary to implement new laws that will regulate its operation.

The Potential Benefits and Challenges of a BRICS+ Agency for Cybersecurity Intelligence Exchange

The Brazil, Russia, India, China, South Africa (BRICS) nations lack a cohesive cybersecurity framework for intelligence exchange. The proposed expansion of the BRICS bloc calls for a BRICS+ agency dedicated to cybersecurity information sharing and analysis. Information Sharing and Analysis Centres (ISACs) are successful not-for-profit entities that centralise resources for gathering, analysing, and disseminating cybersecurity intelligence. However, founding a BRICS+ ISAC confronts challenges such as coordination complexity, financial constraints, trust deficits, linguistic diversity, and disparate legislative landscapes. This paper proposes a novel hybrid ISAC architectural model that amalgamates centralised and decentralised elements, presenting a tailored solution for the multifaceted needs of the expanding BRICS+ entity. The innovation of this model lies in its capacity to enhance cybersecurity resilience, promote efficient intelligence exchange, elevate the BRICS+ international standing, and solidify inter-nation collaboration, while being flexible enough to cater to the specific legal, cultural, and technological variances across member countries. The proposed model's uniqueness and adaptability position it as the premier choice for actualising the BRICS+ vision for a unified cyber front.

Artificial Intelligence In Cyber Security

As cyber threats continue to evolve in complexity and sophistication, the integration of artificial intelligence (AI) in cybersecurity has emerged as a critical frontier for enhancing threat detection, response, and mitigation strategies. This research paper provides a comprehensive examination of the current state of AI applications in cybersecurity, evaluating their strengths, weaknesses, and potential impact on the evolving threat landscape. The study employs a multidimensional methodology, incorporating a thorough literature review, case studies, interviews with cybersecurity experts, and analysis of real-world incidents

Artificial Intelligence in Cybersecurity: Enhancing Threat Detection and Mitigation

Artificial Intelligence in Cybersecurity: Enhancing Threat Detection and Mitigation

Artificial Intelligence in Cyber Security: Review Paper on Current Challenges Faced by the Industry

Artificial Intelligence in Cyber Security: Review Paper on Current Challenges Faced by the Industry

A Survey on Explainable Artificial Intelligence for Cybersecurity

The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of network cybersecurity, XAI has the potential to revolutionize the way we approach network security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity in network systems and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of network-driven cybersecurity threats and issues. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research.

The special features of using the artificial intelligence in the matters of cybersecurity

The role and importance of artificial intelligence in the field of cyber security is determined. The methods of using artificial intelligence in cyber security are detailed. The advantages and priorities of the use of artificial intelligence in the sphere of cyber security are defined. The shortcomings and problematic issues related to the use of artificial intelligence technologies by hackers in the field of cyber security are summarized. The innovative achievements of the practical implementation of generative artificial intelligence technologies ChatGPT (Generative Pre-trained Transformer) are highlighted. The legal principles of the regulation of artificial intelligence in the field of cyber security in Ukraine are outlined. Threatening trends in the use of artificial intelligence technologies have been identified based on the Europol report 2023. A review of the legislative initiatives of the EU aimed at regulating the field of artificial intelligence, in particular in the issue of cyber security, was conducted. The directions of improvement of the legal framework and regulatory requirements regarding the use of artificial intelligence technologies in the field of cyber security are summarized, especially in the conditions of the legal regime of martial law in Ukraine.

Enhancing Security Frameworks with Artificial Intelligence in Cybersecurity

Cybersecurity, in the digital era we live in today, has become a major concern that demands innovation. Data Science and Artificial Intelligence (AI) have played a central role in changing the way we understand and address cyber threats. This research will review the important role of innovation in this technology in improving an organization's ability to detect, prevent, and respond to cyber attacks. Identifying patterns and gaining insights from security events in cyber data, while developing appropriate data-based models, is a key element in realizing automated and intelligent security systems. This research reviews needs in the cyber security domain that can be addressed through Artificial Intelligence (AI) techniques. In this study, we employed quantitative methods to assess the impact of artificial intelligence on enhancing cybersecurity by distributing questionnaires to 85 respondents, which included companies operating in the banking and IT sectors. In addition, this research will explore how data-based intelligent decision-making systems are able to protect systems from known and unknown cyber attacks. This research will conclude by considering the future potential of Artificial Intelligence and cybersecurity.