Cyber Security Intelligence Research Articles

Artificial Intelligence in Cybersecurity : Advancing Threat Modeling and Vulnerability Assessment

This article examines the transformative role of Artificial Intelligence (AI) in revolutionizing cybersecurity practices, with a particular focus on threat modeling and vulnerability assessment. As cyber threats grow increasingly sophisticated, traditional security measures often fall short in providing comprehensive protection. We explore how AI and machine learning algorithms enhance the accuracy and efficiency of threat modeling by analyzing vast datasets to identify patterns and anomalies indicative of potential security risks. The article also investigates AI's contribution to vulnerability assessment, highlighting its capacity for continuous monitoring and real-time analysis of diverse data sources, including network traffic, system logs, and threat intelligence feeds. By simulating various attack scenarios and prioritizing security vulnerabilities, AI-driven tools enable organizations to adopt a more proactive and dynamic approach to cybersecurity. While acknowledging the challenges and ethical considerations associated with AI implementation in this domain, this article underscores the significant potential of AI in fortifying cyber defenses and safeguarding digital assets in an ever-evolving threat landscape.

The editorial for special issue “Russian Research in Artificial Intelligence for Cybersecurity” of Journal of Computer Virology and Hacking Techniques (JICV) highlights the studies of Russian researchers in the field of artificial intelligence to ensure cybersecurity

The editorial for special issue “Russian Research in Artificial Intelligence for Cybersecurity” of Journal of Computer Virology and Hacking Techniques (JICV) highlights the studies of Russian researchers in the field of artificial intelligence to ensure cybersecurity

OPTIMIZED DEEP LEARNING FOR CYBER INTRUSION DETECTION AND SECURED COMMUNICATION IN MANET

The popularity of the Mobile Ad-Hoc Network increases with low expense solutions to the real time applications. The dynamic nature, limited centralized system with low bandwidth often susceptible to the security threads and became the hot topic. To protect the MANET system it is essential to implement a secured system. The purpose of this work is to design an intelligent Cyber Security System (CSS) by collecting the threads data from the MANET system. Initially, the data is pre-processed using the Min-Max normalization technique, followed by the feature selection with the newly approached Mother Optimization algorithm (MOA). The feature selection is effectuated for speedy classification of intrusions and prevent the attacks and therein significantly enhances the security. To provide the security to the MANET system the work proposes a novel Adaptive Deep Belief Network (ADBN). This classifies the attacks and normal data and prevents the system from falling for the threat. Simulation is performed in NS2 and analyzed the effectiveness of the proposed work with existing systems. Our proposed work effectively enhances the security of the MANET system and surpasses all the other works.

An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks

Smart cities have developed advanced technology that improves people’s lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities.

Threats of Artificial Intelligence for Cybersecurity

Artificial intelligence enables automated decision-making and facilitates many aspects of daily life, bringing with it improvements in operations and numerous other benefits. However, AI systems face numerous cybersecurity threats, and AI itself needs to be secured, as cases of malicious attacks have already been reported, e.g. AI techniques and AI-based systems can lead to unexpected results and can be modified to manipulate expected results. Therefore, it is essential to understand the AI threat landscape and to have a common and unifying basis for understanding the potential of threats and consequently to perform specific risk assessments. The latter will support the implementation of targeted and proportionate security measures and controls to counter AI-related threats. This article explores the potential threats posed by AI in cybersecurity and discusses the implications for individuals, organizations, and society at large.

A process mining-based method for attacker profiling using the MITRE ATT&CK taxonomy

Cybersecurity intelligence involves gathering and analyzing data to understand cyber adversaries’ capabilities, intentions, and behaviors to establish adequate security measures. The MITRE ATT&CK framework is valuable for gaining insight into cyber threats since it details attacker tactics, techniques, and procedures. However, to fully understand an attacker’s behavior, it is necessary to connect individual tactics. In this context, Process Mining (PM) can be used to analyze runtime events from information systems, thereby discovering causal relations between those events. This article presents a novel approach combining Process Mining with the MITRE ATT&CK framework to discover process models of different attack strategies. Our approach involves mapping low-level system events to corresponding event labels from the MITRE ATT&CK taxonomy, increasing the abstraction level for attacker profiling. We demonstrate the effectiveness of our approach using real datasets of human and automated (malware) behavior. This exploration helps to develop more efficient and adaptable security strategies to combat current cyber threats and provides valuable guidelines for future research.

Examining the Role of Artificial Intelligence in Cyber Security (CS): A Systematic Review for Preventing Prospective Solutions in Financial Transactions

Examining the Role of Artificial Intelligence in Cyber Security (CS): A Systematic Review for Preventing Prospective Solutions in Financial Transactions

A Survey on Artificial Intelligence in Cybersecurity for Smart Agriculture: State-of-the-Art, Cyber Threats, Artificial Intelligence Applications, and Ethical Concerns

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review described advancements from conventional agriculture to modern SA, including architecture and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber threats that endanger its integrity and dependability and provide a complete analysis of their possible consequences. Still, the research examined the essential role of AI in combating these threats, focusing on its applications in threat identification, risk management, and real-time response mechanisms. The survey also discusses ethical concerns such as data privacy, the requirement for high-quality information, and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers and practitioners with insights into AI’s capabilities and future directions in the security of smart agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by comprehensively evaluating the existing environment and future trends.

An ensemble deep learning-based cyber attack detection system using optimization strategy

Background of the studyCyber attack stories become a routine in which new levels of intention are shown by the cyber attacker through sophisticated attacks on networks. The conventional approaches struggle to detect unknown malware and complex attacks. It does not secure the user's individual information. Hence, deep learning methods are used to effectively predict the attacks. Proposed modelThis paper implements an intelligent cyber security mechanism by employing effective mechanisms. Initially, the significant data for this approach is gathered from the benchmark data sources, and then they are given into the optimal feature chosen. Here, the parameters are optimally chosen with the aid of the Modified Puzzle Optimization Algorithm (MPOA) for the successive outcomes. Moreover, the optimally selected attributes are fed into the Ensemble Serial Cascaded Deep learning with Attention Mechanism (ESCDLAM), which is the integration of the 1 Dimensional Convolutional Neural Networks (1DCNN), Recurrent Neural Network (RNN), and Deep Temporal Convolutional Networks (DTCN). Here, the parameter optimization takes place to improve detection performance. DatasetThe experimentation is conducted through a diverse datasets which includes CICIDS 2017 Friday Noon DDoS 0beefa93–4, NSL-KDD-Dataset, and Kddcup99 for the effective experimental analysis for detecting the cyber attacks. Findings of the resultsThe results outcome of the developed model shows 97 % in terms of accuracy, recall, MCC, and specificity. Also, the precision of the developed model attains a value of 95. ConclusionThe outcomes of the approach are compared with the other pre-existing approaches to show its supremacy.

Research on Intelligent Cybersecurity Protection System in Cloud Computing Environment

In the digital era, cloud computing has transformed data processing, storage, and analysis with its scalability and flexibility. However, it also introduces new security challenges, such as data breaches and unauthorized access, which are exacerbated by Advanced Persistent Threats (APT) and zero-day vulnerabilities. This study aims to construct an intelligent network security protection system that adapts to the dynamic cloud computing environment, leveraging artificial intelligence and machine learning to enhance security efficiency and accuracy. The research objectives include developing a system that maintains privacy protection and user experience while ensuring secure access to cloud services. The paper presents a multi-layered architecture for the intelligent cybersecurity protection system, incorporating data collection, threat detection, access control, and adaptive security policies. Experimental methods and results validate the system’s effectiveness, demonstrating improved detection rates and reduced false positives compared to existing solutions. The study concludes with a discussion on the model’s advantages, limitations, and future application prospects in the field of cloud computing security.

Artificial Intelligence in Cybersecurity

Artificial Intelligence in Cybersecurity

Cyber Risk Assessment Framework for the Construction Industry Using Machine Learning Techniques

Construction 4.0 integrates digital technologies that increase vulnerability to cyber threats. A dedicated cyber risk assessment framework is essential for proactive risk mitigation. However, existing studies on this subject within the construction sector are scarce, with most discussions still in the preliminary stages. This study introduces a cyber risk assessment framework that integrates machine learning techniques, pioneering a data-driven approach to quantitatively assess cyber risks while considering industry-specific vulnerabilities. The framework builds on over 20 literature reviews related to construction cybersecurity and semi-structured interviews with two industry experts, ensuring both rigor and alignment with practical industrial needs. This study also addresses the challenges of data collection and proposes potential solutions, such as a standardized data collection format with preset fields that computers can automatically populate using data from construction companies. Additionally, the framework proposes dynamic machine learning models that adjust based on new data, facilitating continuous risk monitoring tailored to industry needs. Furthermore, this study explores the potential of advanced language models in cybersecurity management, positioning them as intelligent cybersecurity consultants that provide answers to security inquiries. Overall, this study develops a conceptual machine learning framework aimed at creating a robust, off-the-shelf cyber risk management system for industry practitioners.

Impact of Artificial Intelligence in Cyber Security

Impact of Artificial Intelligence in Cyber Security

Beyond Conventional Threat Defense: Implementing Advanced Threat Modeling Techniques, Risk Modeling Frameworks and Contingency Planning in the Healthcare Sector for Enhanced Data Security

This research study explores the integration of advanced threat and risk modeling with scenario and contingency planning to enhance security and resilience within healthcare organizations. Given the escalating complexity and frequency of cyber threats targeting the healthcare sector, the study assesses the efficacy of a synergistic cybersecurity approach. Utilizing a quantitative research methodology, data were collected through a survey administered to 452 healthcare practitioners, focusing on their perceptions and experiences with cybersecurity threats, risk modeling techniques, and the effectiveness of scenario and contingency planning. The survey incorporated a structured questionnaire utilizing Likert scale closed-ended questions, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed four hypotheses related to the impact of advanced threat and risk modeling and scenario and contingency planning on the cybersecurity posture and operational resilience of healthcare organizations. Results indicated a significant positive relationship between advanced threat modeling and scenario planning with the cybersecurity posture of healthcare organizations, which, in turn, notably enhances organizational resilience. Specifically, the study found that integrating advanced threat and risk modeling with scenario and contingency planning significantly improves the ability of healthcare organizations to identify, assess, and mitigate cyber threats effectively. Furthermore, the findings suggest that such integration contributes to more informed decision-making, improved information security management practices, and more effective and efficient digital crime investigation processes. The research study concludes that a comprehensive approach integrating advanced threat and risk modeling with scenario and contingency planning significantly enhances healthcare organizations' cybersecurity posture and operational resilience. The study recommends adopting an integrated cybersecurity strategy, continuous cybersecurity education for all organizational members,regular cybersecurity assessments, and fostering collaboration for enhanced cybersecurity intelligence.

The Role of Artificial Intelligence in Cyber Security

The research paper explores the pivotal role of artificial intelligence (AI) in bolstering cyber security threat detection and protection measures. By leveraging advanced AI techniques such as machine learning, deep learning, and behavioral analysis, organizations can effectively identify and mitigate cyber threats in real-time. AI enables proactive threat detection by analyzing vast amounts of data, detecting patterns, and predicting potential security incidents. Moreover, AI-driven security solutions offer scalability, efficiency, and adaptability, making them indispensable in combating the evolving cyber threat landscape. Overall, AI plays a crucial role in fortifying cyber security defenses and safeguarding critical assets against sophisticated cyberattacks. As cyber threats continue to grow in complexity and frequency, the integration of AI into security frameworks will not only enhance response times but also empower organizations to stay one step ahead of potential attacks Furthermore, as organizations increasingly adopt AI-driven security measures, the importance of integrating ethical considerations and addressing potential biases in AI algorithms becomes paramount. While these technologies can significantly enhance threat detection capabilities, they also risk perpetuating existing inequalities if not meticulously designed and monitored. For instance, biased data sets may lead to disproportionate scrutiny of certain user groups, undermining trust in security systems. Additionally, the dynamic nature of cyber threats necessitates continuous learning and adaptation from AI systems, highlighting the need for ongoing research and collaboration among cybersecurity professionals, technologists, and ethicists alike. By fostering a holistic approach that emphasizes both technological advancement and ethical responsibility, organizations can better navigate the complexities of modern cyber environments while enhancing their overall security posture

Artificial Intelligence in Cybersecurity

The usage of Internet as increased with time ,but with the increase in usage of internet ,the cases of Cybercrime has also gone up. However, with increase of artificial intelligence ,the companies and business are starting to look for AI tools to help against cybercrime .AI is becoming an essential component of every business. Cybercrime is one of the important sectors where AI has begun demonstrating valuable inputs. It is due to the fact that AI is faster than humans to take action and make an alternate plan of action to protect business and send warning against cybercrime. We will discuss recent cyber crime and how AI is used in the industry to defend itself in the long run

Artificial intelligence in the sphere of cybersecurity: innovations, challenges, and development prospects

The relevance of the study is due to the need for effective protection of information systems in the face of ever-growing cyber threats. In the modern digital world, where cyberattacks are becoming increasingly complex and widespread, the use of artificial intelligence in cybersecurity is extremely relevant. The problem of cybersecurity is becoming increasingly important and conducting relevant research is becoming a necessary aspect of ensuring security in the digital space.In this context, we propose an article that examines the role of artificial intelligence, innovations, challenges, and prospects of its development in the sphere of cybersecurity. Additionally, it explores the utilization of artificial intelligence for automating processes of detecting, analysing, and responding to cyber threats, and its practical significance for enhancing security levels in the digital space and the effectiveness of cyber threat protection. The main approaches for this study are trend analysis and expert assessments, which allow for a comprehensive review of the directions of artificial intelligence development in the field of cybersecurity, the benefits and challenges of its implementation, as well as potential opportunities and threats to information security. The article discusses advanced security information and event management systems designed to collect, analyse and respond to cyber threats and other systems aimed at specific aspects ofprotection and expanding automation capabilities to improve the security level of information systems. The materials of the article are of theoretical value for further scientific research on the use of artificial intelligence in the sphere of cybersecurity and reflect important features of its influence on this area. This research can contribute not only to deepening the understanding of the theoretical aspects of the use of artificial intelligence in cybersecurity but also to identifying specific practical applications and innovations that will help improve protection against cyber threats in the future.

The Role of Artificial Intelligence in Cyber Security

Without significant automation, humans are unable to manage the volume of data and the complexity of procedures required to safeguard cyberspace. Creating software and systems with typical fixed implementations (hardwired decision-making logic) that successfully prevent security threats is difficult. This problem can be solved with machine simplicity and AI learning approaches. This study assesses whether strengthening defensive mechanisms can improve cybersecurity capabilities and offers a brief summary of artificial intelligence (AI) applications of various cybersecurity using artificial technologies. We can determine that there are currently useful applications by looking at the most recent cybersecurity software that makes use of artificial intelligence. Neural networks are their primary tool for protecting the periphery and other cybersecurity domains. Nevertheless, it was evident that some cybersecurity problems would require the application of artificial intelligence technology. For instance, thorough information is needed for strategic decision-making, and logical decision support is one of the unmet cybersecurity needs.

Intelligent cybersecurity approach for data protection in cloud computing based Internet of Things

Intelligent cybersecurity approach for data protection in cloud computing based Internet of Things

Application and challenges of artificial intelligence in cybersecurity

This paper delves into the multi-faceted role of Artificial Intelligence (AI) in the field of cybersecurity. It conducts an in-depth analysis of two common security problems, spam email and DDos/Dos attacks. It focuses on the application of AI in resolving these problems and further on Event Management (SIEM) and Intrusion Detection Systems (IDS). Furthermore, this article outlines and discusses five key challenges that AI may encounter throughout its development, including issues related to reliability, unidentified threats, data privacy, ethical considerations, and the importance of an explainable AI. The paper underscores the remarkable potential of AI to improve our defences against emerging cybersecurity threats. It highlights the growing importance of addressing ethical and legal issues in the evolving technological environment. Finally, the article offers a comprehensive summary and valuable insights into the future prospects of AI in the realm of cybersecurity, confirming its crucial role in shaping the security landscape in the digital era.