Citation

Khan, Salman A. and Raza, Hasnat and Alam, Mansoor (2026) AI-Driven Malware Analysis and Detection: A Comprehensive Survey of Techniques, Trends and Challenges. Journal of Informatics and Web Engineering, 1 (5). p. 106. ISSN 2821-370X

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Abstract

Malware represents the most critical threat in cybersecurity, meant to compromise the security for any individual or any organization. These are covert software, designed to perform malicious act like data theft, data alteration, and to interrupt a normal operation of the services. The persistent evolution of malware has called for more sophisticated techniques in its detection and prevention, resulted into direct need of Artificial Intelligence in cybersecurity. Artificial intelligence, using machine learning techniques and rising concepts like neural networks has greatly improved the traditional static and dynamic ways of detecting malware. Advances in AI-driven solutions have made them much more capable than their predecessors of detecting malware and addressing threats in real time. By training machine learning models on vast quantities of data, malicious patterns can easily be detected and identify patterns. With these emerging challenges, AI powers automated real-time analysis and adaptive security posture can effectively mitigate the threat. Large Language Models (LLMs) have revolutionized natural language processing and are increasingly being deployed across a wide range of applications, including text generation, summarization, translation, and detection systems. Recent research related to the methodologies employed in developing detection systems using LLMs, outlines the existing limitations and research gaps, and proposes potential areas for future investigation. The use of AI in malware analysis faces its own challenges with the potential for adversarial attacks and the scale of AI models that can muddy the waters of transparency and trust. Overcoming these challenges will involve the creation of mature, ethical, AI systems and an open dialogue between cybersecurity professionals, sustainable AI development and regulatory compliance all working in concert.

Item Type:	Article
Uncontrolled Keywords:	Adaptive Security, Adversarial Attacks, Artificial Intelligence, Cybersecurity, Dynamic Analysis, Malware, Machine Learning, Polymorphic Malware, Static Analysis, Large Language Models
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Others
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	09 Jul 2026 00:48
Last Modified:	09 Jul 2026 00:48
URII:	http://shdl.mmu.edu.my/id/eprint/16280

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