Guest Editors
Prof. Dr. Guanfeng Liu
Email: guanfeng.liu@mq.edu.au
Affiliation: School of Computing, Macquarie University, NSW 2109, Australia
Homepage:
Research Interests: graph neural networks, trust and security, graph data mining, recommender systems
Dr. Yang Zhang
Email: yang.zhang@unt.edu
Affiliation: Department of Data Science, College of Information, University of North Texas, Denton, TX 76207, USA
Homepage:
Research Interests: natural language processing, trust management, IoT
Prof. Dr. An Liu
Email: anliu@suda.edu.cn
Affiliation: School of Computer Science and Technology, Soochow University, Soochow, 215006, China
Homepage:
Research Interests: data privacy and security, temporal data analysis, artificial intelligence
Summary
With the rapid expansion of digital infrastructure and the growing complexity of cyber threats, cybersecurity has become a critical global concern. Traditional security mechanisms struggle to keep pace with sophisticated cyber-attacks, necessitating the integration of Artificial Intelligence (AI) to enhance intrusion detection and threat analysis. AI-driven approaches, including machine learning, deep learning, and reinforcement learning, offer advanced capabilities in detecting, analyzing, and mitigating cyber threats in real-time. This Special Issue aims to explore cutting-edge AI techniques that improve cybersecurity defenses, strengthen threat intelligence, and ensure robust digital protection.
The scope of this Special Issue includes AI-driven methodologies for anomaly detection, network security, malware analysis, and real-time threat response. We encourage original research and review articles focusing on novel AI frameworks, hybrid approaches, and the ethical and privacy considerations in AI-powered cybersecurity solutions.
Suggested Themes:
• AI and machine learning for intrusion detection systems (IDS)
• Deep learning approaches for malware detection and classification
• Adversarial AI and its implications for cybersecurity
• AI-driven behavioral analysis for anomaly detection
• Privacy-preserving AI techniques in threat intelligence
• Cyber threat prediction and risk assessment using AI
• Explainable AI (XAI) for transparent and accountable cybersecurity
• AI applications in cloud, IoT, and edge security
Keywords
AI in Cybersecurity, Privacy-Preserving AI, Deep Learning for Cybersecurity, Anomaly Detection, Cyber Threat Prediction and Prevention, Intrusion Detection Systems (IDS)
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