Guest Editors
Dr. Bander Ali Saleh Al-rimy
Email: bnder321@gmail.com
Affiliation: School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, United Kingdom
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Research Interests: malware analysis and intrusion detection, iot/network security and reliability, cyber threat intelligence, privacy and data protection, computer forensics, blockchain, data science, ai, and machine learning
Prof. Fredrick Sheldon
Email: sheldon@uidaho.edu
Affiliation: Department of Computer Science, University of Idaho, Moscow, ID 83844, USA
Homepage:
Research Interests: information assurance and security, formal methods, specification, model checking, closed form and stochastic analysis, software requirements, design, development and testing of high assurance systems, cryptographic key management, supply chain and authentication, security policy compliance, hazards/vulnerabilities and risk assessment, cyber physical systems, failure scenarios and penetration testing, cybernomics
Summary
Malware remains a dominant threat in today's cybersecurity landscape, continuously evolving to evade traditional detection techniques. With the increasing sophistication of attack vectors—ranging from polymorphic ransomware to fileless malware—there is a critical need for more intelligent, adaptive solutions. Artificial Intelligence (AI) offers promising capabilities for enhancing malware analysis, digital forensics, and proactive detection, enabling systems to respond in real time to emerging threats and complex attack patterns.
Aim and Scope
This Special Issue focuses on the latest research and practical advancements in AI-driven malware analysis and detection. It aims to gather innovative contributions that address the technical, operational, and ethical challenges of deploying AI in cybersecurity. We seek submissions that propose novel models, scalable frameworks, and interdisciplinary approaches with demonstrated effectiveness in real-world scenarios. The goal is to promote adaptive, explainable, and efficient solutions that strengthen the defense of critical digital assets.
Suggested Themes:
· AI-based malware detection algorithms and models
· Feature engineering and data preprocessing for malware analysis and Forensics
· Integration of AI with intrusion detection and network security systems
· LLM-powered malware detection and automated response and forensics
· Adversarial machine learning and model robustness
· Scalable and real-time AI-Based Malware Forensics
· Explainable AI and ethical implications in malware detection
Keywords
Malware Detection, Artificial Intelligence, Machine Learning, Cybersecurity, Digital Forensics, Adversarial Machine Learning, Feature Engineering, Large Language Models (LLMs), Intrusion Detection Systems (IDS)
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