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AI-Powered Malware Detection and Behavioral Analysis
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Author(s): P. Selvakumar (Department of Science and Humanities, Nehru Institute of Technology, Coimbatore, India), Vaishali Rahate (Datta Meghe Institute of Management Studies, India), A. S. Deeppana (Karpagam Academy of Higher Education, India), Yukta Sawalkar (Datta Meghe Institute of Management Studies, India), C. John Paul (St. Joseph University, India)and S. Murugaveni (SRM Institute of Science and Technology, India)
Copyright: 2026
Pages: 30
Source title:
Examining Vulnerabilities and Adversarial Exploitation of AI and LLMs
Source Author(s)/Editor(s): Puya Pakshad (Illinois Institute of Technology, USA)and Marwan Omar (Illinois Institute of Technology, USA)
DOI: 10.4018/979-8-3373-8252-4.ch002
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Abstract
In the rapidly evolving landscape of cybersecurity, malware continues to pose significant threats to individuals, enterprises, and critical infrastructures. Traditional signature-based detection techniques, though effective against known threats, fall short when confronted with sophisticated, polymorphic, and zero-day malware. This limitation has fueled research into more intelligent, adaptive detection mechanisms that can identify malicious software even when it exhibits novel patterns or obfuscation strategies. Static malware analysis, unlike dynamic analysis, focuses on examining the intrinsic attributes of executable files without executing them, making it safer, faster, and less resource-intensive. Static features typically include opcode sequences, bytecode patterns, control flow graphs, API call frequency distributions, file headers, string literals, and metadata extracted from Portable Executable (PE) files or other binary formats.
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