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In response to the evolution of second-generation malwares, adept at circumventing traditional detection techniques, there has been an imperative and ever-growing demand of advanced solutions for effective malware detection. This comprehensive review assesses and analyzes the efficiency of machine learning techniques namely data mining, neural networks, and hidden Markov model in the detection of polymorphic malware. Our analysis has meticulously compared the pros and cons of each approach and provided insights into areas of improvement to seek optimized solutions to counter the threats faced by malwares attacks.

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Machine Learning Approaches for Polymorphic Malware Detection: A Comprehensive Review

  • 会议论文

  • 发表日期：

    2025年

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  • Het Amrishbhai Valera,
  • Chetna Vijay Rai,
  • Jainil Shah

摘要

In response to the evolution of second-generation malwares, adept at circumventing traditional detection techniques, there has been an imperative and ever-growing demand of advanced solutions for effective malware detection. This comprehensive review assesses and analyzes the efficiency of machine learning techniques namely data mining, neural networks, and hidden Markov model in the detection of polymorphic malware. Our analysis has meticulously compared the pros and cons of each approach and provided insights into areas of improvement to seek optimized solutions to counter the threats faced by malwares attacks.