Cyber Security Intelligence Research Articles

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.

Artificial Intelligence for Cybersecurity

Artificial Intelligence for Cybersecurity

Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy

Knowledge-Enhanced Neurosymbolic Artificial Intelligence for Cybersecurity and Privacy

Artificial Intelligence

As we navigate the digital era of the 21st century, cyber security has grown into a pressing societal issue that requires innovative, cutting-edge solutions. In response to this pressing need, Artificial Intelligence (AI) has emerged as a revolutionary instrument, causing a paradigm shift in cyber security. AI's prowess resides in its capacity to process and analyze immense quantities of heterogeneous cyber security data, thereby facilitating the efficient completion of crucial tasks. These duties, which include threat detection, asset prioritization, and vulnerability management, are performed with a level of speed and accuracy that far exceeds human capabilities, thereby transforming our approach to cyber security. This document provides a comprehensive dissection of AI's profound impact on cyber security, as well as an in-depth analysis of how AI tools not only augment, but in many cases transcend human-mediated processes. By delving into the complexities of AI implementation within the realm of cyber security, we demonstrate the potential for AI to effectively anticipate, identify, and preempt cyber threats, empowering organizations to take a proactive stance towards digital safety. Despite these advancements, it is essential to consider the inherent limitations of AI. We emphasize the need for sustained human oversight and intervention to ensure that cyber security measures are proportionate and effective. Importantly, we address potential ethical concerns and emphasize the significance of robust governance structures for the responsible and transparent use of artificial intelligence in cyber security. This paper clarifies the transformative role of AI in reshaping cyber security strategies, thereby contributing to a safer, more secure digital future. In doing so, it sets the groundwork for further exploration and discussion on the use of AI in cyber security, a discussion that is becoming increasingly important as we continue to move deeper into the digital age.

Keystroke Biometric Data for Identity Verification: Performance Analysis of Machine Learning Algorithms

Cyber-attacks are on the rise in today's environment, where traditional security measures are ineffective. As a result, the adoption of cutting-edge tools such as artificial intelligence technology is critical in the fight against cyber threats. User behaviors, such as keyboard dynamics, provide potential data that can be used for protection against cyber-attacks. Keystroke dynamics is one of the fastest and most cost-effective methods that can be used to detect user behaviors, as it can be captured using standard user keyboards. The analysis of this data and protection against cyber-attacks is made possible through machine learning algorithms. Based on keyboard dynamics, this study analyzes the performance of k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Neural Network (NN) methods for user behavior analysis and anomaly detection. The findings shed light on the significance of artificial intelligence in cyber security by examining the accomplishments of several machine learning algorithms. The study's findings may serve as a foundation for future research and novel solutions in the realm of cyber security.

Artificial Intelligence In Cyber Security: Rescue Or Challenge

Objective: The objective of this research paper is to explore the potential of artificial intelligence (AI) in enhancing and fortifying cybersecurity measures. In light of the increasing cyber threats and the limitations of traditional computer security systems, the paper aims to shed light on the concept of AI and its components. The central goal is to demonstrate how AI can be leveraged to improve cybersecurity, focusing on key areas such as machine learning, data mining, in-depth learning, and expert programs.
 Method: This research paper utilizes a literature review approach to investigate the intersection of artificial intelligence and cybersecurity. It involves analyzing existing scholarly works and studies to understand the challenges posed by evolving cyber threats and the limitations of conventional security systems. The paper identifies various components of AI that have the potential to enhance cybersecurity measures and delineates their applicability in the digital landscape.
 Results: The analysis of past literature emphasizes the growing significance of adopting artificial intelligence techniques to bolster cybersecurity practices. The results underscore the inadequacies of traditional security systems in predicting and countering the ever-evolving tactics of cyber attackers and terrorists. Through an examination of various AI components, including machine learning, data mining, in-depth learning, and expert programs, the paper demonstrates the potential benefits that these technologies can offer to the field of cybersecurity.
 Conclusions: In conclusion, this research paper highlights the escalating threats to cybersecurity in the face of rapid technological advancement. The imperative of addressing these challenges through AI becomes evident as traditional security systems prove insufficient. By harnessing the power of artificial intelligence techniques such as machine learning, data mining, in-depth learning, and expert programs, institutions can significantly enhance their cybersecurity capabilities. The paper underscores the need for a paradigm shift towards AI-driven cybersecurity strategies to effectively predict, prevent, and counteract cyber threats in the digital age.

The role of Artificial Intelligence in Cyber Security and Incident Response

The escalating number and intricacy of cyber attacks have underscored the urgent requirement for innovative solutions to bolster the security of digital infrastructure. Among these solutions, Artificial Intelligence (AI) emerges as a promising technology with the potential to significantly enhance cyber security and incident response. It has the aptitude to improve the speed and accuracy of threat detection, response and mitigation while also reducing the workload on security professionals. This research paper focuses on the role of AI in key areas of cyber security and incident response, specifically vulnerability assessment, intrusion detection and prevention, and digital forensics analysis. It elucidates how AI, with its innate capabilities, can be a game-changer by empowering organizations to detect, respond to, and mitigate threats more effectively. However, AI is not a silver bullet for Cyber Security. Like any technology, it possesses its limitations and potential vulnerabilities. Consequently, this paper also addresses the need for ongoing development in the field of AI to overcome these limitations and challenges. By recognizing the need for continuous advancements, the research paper emphasizes the importance of future research and development efforts to maximize the potential benefits of AI in the realm of cyber security.

A Malware Detection and Extraction Method for the Related Information Using the ViT Attention Mechanism on Android Operating System

Artificial intelligence (AI) is increasingly being utilized in cybersecurity, particularly for detecting malicious applications. However, the black-box nature of AI models presents a significant challenge. This lack of transparency makes it difficult to understand and trust the results. In order to address this, it is necessary to incorporate explainability into the detection model. There is insufficient research to provide reasons why applications are detected as malicious or explain their behavior. In this paper, we propose a method of a Vision Transformer(ViT)-based malware detection model and malicious behavior extraction using an attention map to achieve high detection accuracy and high interpretability. Malware detection uses a ViT-based model, which takes an image as input. ViT offers a significant advantage for image detection tasks by leveraging attention mechanisms, enabling robust interpretation and understanding of the intricate patterns within the images. The image is converted from an application. An attention map is generated with attention values generated during the detection process. The attention map is used to identify factors that the model deems important. Class and method names are extracted and provided based on the identified factors. The performance of the detection was validated using real-world datasets. The malware detection accuracy was 80.27%, which is a high level of accuracy compared to other models used for image-based malware detection. The interpretability was measured in the same way as the F1-score, resulting in an interpretability score of 0.70. This score is superior to existing interpretable machine learning (ML)-based methods, such as Drebin, LIME, and XMal. By analyzing malicious applications, we also confirmed that the extracted classes and methods are related to malicious behavior. With the proposed method, security experts can understand the reason behind the model’s detection and the behavior of malicious applications. Given the growing importance of explainable artificial intelligence in cybersecurity, this method is expected to make a significant contribution to this field.

Intelligent AI-based Healthcare Cyber Security System using Multi-Source Transfer Learning Method

Cyber-security intelligence have made a great impact over healthcare industry where several researchers are developing new techniques to improve security for healthcare systems. Besides, Artificial Intelligence (AI) become the tremendous technology in recent decades to improve the existing methods to be more intelligent. In this paper, we proposed cyber attack detection system for healthcare sector with centralized and federated transfer learning mode. Edge of Things (EoT) framework is developed in connection with cloud and healthcare sectors to transmit the data efficiently and the proposed Centralized with Multi-Source Transfer Learning (CMTL) algorithm which is used for detection and classification of various threats such as information gathering, DoS/DDoS attacks, Malware attacks, Injection attacks, and Man in the Middle attacks. Performance of the proposed framework is evaluated using various datasets such as EMNIST, X-IIoTID, and Federated TON_IoT. Our framework outperforms with the analysis of execution time and obtains high level accuracy when compared with different algorithms.

A REVIEW ON ARTIFICIAL INTELLIGENCE TECHNIQUES IN PREVENTING CYBER THREATS

Artificial intelligence advancements in the present day are proving to be the most effective method for preventing cyberattacks. Artificial intelligence (AI) is being used by experts as a protection against cyberattacks. This technology is being used by security analysts to spot abnormalities, which reduces time and reduces total company expenditures. With the proliferation of IoT and linked devices in today's digital world, cyber security professionals are constantly confronted with new difficulties. The expert needs all available tools to stop attacks, discover security problems, and respond to attacks. The greater standard of technology in today's environment is strongly correlated with the rise in cybersecurity risks. The digitalization of organizations makes it essential to focus on cybersecurity issues and ways to make them better. Using artificial intelligence in cybersecurity is essential to improve data protection because traditional computer algorithms may occasionally be inadequate to manage all cyber threats. A descriptiveanalytical approach from previous research on the application of AI in cybersecurity is used. This paper aims to emphasise artificial intelligence and the concepts of cybersecurity that can be developed to improve the security of the data. Methods based on artificial intelligence can enhance the overall security operation of traditional security systems and give higher security against an increasing array of complicated cyber threats, when traditional security technologies may be incompetent or ineffective. In this study, we look at how human reasoning and artificial intelligence (AI) can be leveraged to improve cyber security. This study's primary goal is to showcase current developments in the use of AI approaches to defend against cyberattacks.

Artificial Intelligence in Cybersecurity: A Survey of National Research, Investment and Policy Implementation

Artificial Intelligence in Cybersecurity: A Survey of National Research, Investment and Policy Implementation

Artificial intelligence for cybersecurity: Literature review and future research directions

Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen the security posture against various security issues and cyberattacks. This article presents a systematic literature review and a detailed analysis of AI use cases for cybersecurity provisioning. The review resulted in 2395 studies, of which 236 were identified as primary. This article classifies the identified AI use cases based on a NIST cybersecurity framework using a thematic analysis approach. This classification framework will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in different contexts. The review also identifies future research opportunities in emerging cybersecurity application areas, advanced AI methods, data representation, and the development of new infrastructures for the successful adoption of AI-based cybersecurity in today's era of digital transformation and polycrisis.

From Bombs to Bytes: Role of Cyber Security in National Security and Importance of Artificial Intelligence in Cyber Security

Bu çalışma, devletin siber ortamda depoladığı veri miktarının artması ve kamu hizmetlerinin giderek daha fazla siber ortam aracılığıyla sunulması nedeniyle siber güvenliğe; siber güvenliğin sağlanmasında ise gerekli hız, çeviklik ve esnekliği sağlayan bir araç olarak görülen yapan zekâya odaklanmaktadır. Çalışmanın amacı ise siber güvenliğin neden bir ulusal güvenlik bileşeni olduğunun ve yapay zekânın siber güvenlikteki öneminin araştırılması, siber güvenlik alanında ülke uygulamalarından örnekler sunarak bunlardan çıkarımlar yapılmasıdır. Literatür taraması yapılarak hazırlanan bu çalışma sonucunda kamu sektörünün (devletin) siber saldırılardan en çok etkilenen alan olduğu; siber saldırıların askeri gücün operasyon sahasına yansıtılmasını etkileyebileceği; siber saldırıların ve siber ortama artan bağımlılığın toplum ve devleti bir arada tutan “güven” duygusunu baltalayabileceği; günümüzde siber saldırılarda yapay zekâ kullanımının kısıtlı olduğu ancak gelecekte kullanımının artacağı öngörüsü nedeniyle geleceğin siber güvenliğinin yapay zekâ odaklı hale geleceği gibi hususların önem taşıdığı görülmüştür. Buradan hareketle siber saldırıların ulusal güvenliği tehdit edebilecek potansiyelinin bulunduğu ve siber güvenliğin bir ulusal güvenlik bileşeni olduğu anlaşılmaktadır. Çalışmanın sonuç bölümünde de ülkelerin siber güvenlik uygulamalarından hareketle siber güvenliğin sağlanmasına yönelik önerilere yer verilmiştir.

ARTIFICIAL INTELLIGENCE AND CYBERSECURITY

The article introduces the concept of artificial intelligence and identifies its main applications inthe field of cybersecurity. The main problems of scale, context, precision, accuracy, and speed aredescribed. The types of problems solved by machine learning are identified. Attention was paid tothe risks and limitations of using artificial intelligence in cybersecurity and the problems ofimplementing it in an organisation.

Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

AbstractDue to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber‐attacks, threats, risks, damages, or unauthorized access. Artificial intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real‐world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI‐based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this article, we provide a comprehensive view on “Cybersecurity Intelligence and Robustness,” emphasizing multi‐aspects AI‐based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero‐day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI‐based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Sixth-generation (6G)-based communications have many applications and are emerging as a new system to utilize existing vehicles and communication devices in autonomous vehicles (AVs). Electric vehicles and AVs not supporting the integration of intelligent cybersecurity will become vulnerable, and their internal functions, features, and devices providing services will be damaged. This paper presents an intelligent cybersecurity model integrating intelligent features according to the emerging 6G-based technology based on evolving cyberattacks. The model’s novel design was developed using the necessary algorithms to provide quick and proactive decisions with intelligent cybersecurity based on 6G (IC6G) policies when AVs face cyberattacks. In this model, network security algorithms incorporating intelligent techniques are developed using applied cryptography. Money transaction handling services implemented in an AV are considered an example to determine the security and intelligence level depending on the IC6G policies. Intelligence, complexity, and energy efficiency (EE) are assessed. Finally, we conclude that the model results are effective for intelligently detecting and preventing cyberattacks on AVs.

Dynamic Capabilities in Cybersecurity Intelligence: A Meta-Synthesis to Enhance Protection Against Cyber Threats

Advanced cybersecurity threats with automated capabilities are on the rise in industries such as finance, healthcare, technology, retail, telecoms, and transportation, as well as government. It is necessary to conduct analyses of cybersecurity-related resources and capabilities to build cybersecurity intelligence (CI). The purpose of this paper is to suggest a dynamic capability in a cybersecurity intelligence (DCCI) model based on existing literature that helped firms reduce risks of cyber violations and advance the development of systems and the life cycle of firms. Through a meta-synthesis, an abduction and induction approach through eight methodological steps analyzed in forty-seven case studies the presence of cybersecurity capabilities to build CI. Combining theoretical and practical information security maturity models as a foundation, we understand capabilities building to improve the predictability of cyber incidents. The results evidenced four second-order dimensions to build CI named doing, enabling, improving, and managing cybersecurity, and eight first-order outcomes to represent the DCCI model. This research makes an unprecedented contribution to international and national scenarios, as it will allow firms to innovate their resource management processes and abilities to enable better cybersecurity projects and reduce the impacts of potential cyberattacks with the probability of eradicating vulnerabilities.

Survey on the role of artificial intelligence in cyber security

The survey focuses on the role of Artificial Intelligence (AI) in enhancing cyber security, as well as the challenges that come with it. AI has become a crucial component in addressing the increasing sophistication of cyber-attacks and malware. Implementing intelligent cyber security measures, especially in support, situation awareness, and data management domains, is necessary for optimal effectiveness. Professional system technology is a promising technology in areas where neural networks may not be the most suitable technology. While the timeline for universal computing advances is unclear, it is important to note that attackers can potentially use such technology as soon as it becomes available. Therefore, advancements in machine learning, knowledge understanding, illustration, and handling can significantly enhance a system's capacity for cyber security. This article provides a comprehensive review of AI and computer applications in cyber security and offers recommendations for improving security system intelligence.

Integrating artificial intelligence in cyber security for cyber-physical systems

<abstract><p>Due to the complexities of systems thinking and the communication between independent Cyber-Physical Systems (CPSs) areas through accumulative expansion, several security threats are posed, such as deception of channels for information sharing, hardware aspects and virtual machines. CPSs have become increasingly complex, sophisticated, knowledgeable and fully independent. Because of their complex interactions between heterogeneous virtual and objective components, CPSs are subject to significant disturbances from intended and unintended events, making it extremely difficult for scientists to predict their behavior. This paper proposes a framework for Cyber-Physical Business Systems based on Artificial Intelligence (CPBS-AI). It summarizes several safety risks in distinct CPS levels, their threat modeling and the scientific challenges they face in building effective security solutions. This research provides a thorough overview of current state-of-the-art static capable of adapting detection and tracking approaches and their methodological limitations, namely, the difficulty of identifying runtime security attacks caused by hibernation or uncertainty. The way of identifying the threat and the security attacks in networks reduce the complexities in the communication in CPS. The negligible threats exhibit an inability to be identified, avoided and blocked by Intrusion Prevention Security Systems (IPSSs), and misbehavior in the database of the safety measures is analyzed. Neural Networks (NN) and Variable Structure Control (VSC) are designed to estimate attacks and prevent the risk of threats in tracking applications using a nonlinear monitoring system based on VSC. NN and the VSC evaluate the different attacks based on the nonlinear monitoring system. The evaluation of the proposed CPBS-AI is based on the request time analysis, accuracy, loss and reliability analysis. The overall effectiveness of the system is about 96.01%.</p></abstract>