Guest Editors
Dr. Sathishkumar V E
Email: sathishv@sunway.edu.my
Affiliation: Faculty of Engineering and Technology, Sunway University, Bandar Sunway, Malaysia
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Research Interests: data mining, machine learning, artificial intelligence
Prof. Malliga Subramanian
Email: mallisenthil@kongu.ac.in
Affiliation: Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, India
Homepage:
Research Interests: image processing, deep learning, natural language processing
Summary
The rapid expansion of digital infrastructures, cloud computing environments, Internet of Things (IoT), and intelligent cyber-physical systems has significantly increased exposure to sophisticated malware attacks. Modern malware variants employ polymorphism, obfuscation techniques, fileless execution, and AI-assisted attack strategies, making traditional signature-based detection approaches ineffective. Consequently, intelligent and adaptive malware detection mechanisms have become a critical research priority for ensuring secure digital transformation.
This Special Issue aims to present recent advances in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, and data-driven cybersecurity frameworks for next-generation malware detection and cyber threat intelligence. The issue will focus on innovative methodologies capable of detecting, analyzing, predicting, and mitigating evolving malware threats across distributed and heterogeneous computing environments.
Contributions addressing both theoretical developments and real-world deployment scenarios are encouraged, including behavioral analysis, adversarial learning, explainable security analytics, federated threat intelligence, and automated incident response systems. Emphasis will be placed on scalable, intelligent, and privacy-preserving security solutions applicable to industrial IoT, cloud platforms, edge computing environments, and critical infrastructures.
This Special Issue seeks to bridge the gap between advanced computational intelligence techniques and practical cybersecurity applications, fostering resilient and trustworthy digital ecosystems.
Suggested Topics
· AI and Machine Learning-Based Malware Detection
· Deep Learning for Malware Classification and Analysis
· Behavioral and Dynamic Malware Analysis
· Malware Detection in IoT and Edge Computing Environments
· Cloud and Distributed System Malware Defense
· Adversarial Attacks and Robust Malware Detection Models
· Explainable AI for Cybersecurity and Threat Intelligence
· Federated Learning for Collaborative Malware Detection
· Ransomware Detection and Prevention Techniques
· Zero-Day Attack Detection Mechanisms
· Malware Visualization and Threat Analytics
· Automated Incident Response Using Intelligent Systems
· Blockchain-Enabled Secure Threat Intelligence Sharing
· Hybrid Static–Dynamic Malware Detection Approaches
Keywords
malware detection; cybersecurity; artificial intelligence; machine learning; deep learning; threat intelligence; ransomware detection; IoT security; adversarial learning; explainable AI; cyber threat analytics; secure computing
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