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This chapter delves into the use of AI and machine learning techniques to assess the likelihood of a portable executable (PE) file being malicious. It begins with an exploration of PE files, including their sections and structural components, followed by a discussion on the methodologies used to analyze these files for potential malicious behavior. Additionally, it covers the classification of malware and the strategies employed for this purpose. The core of the chapter is centered around training a machine learning model for malware detection, with an in-depth analysis of GANs, their architecture, and the specific variants used in this study. The chapter concludes by presenting the results from implementing GANs, highlighting their effectiveness and the broader implications for strengthening cybersecurity.

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AI-Enhanced Malware Detection: Advancing Security Through Intelligent Threat Identification

  • 章节

  • 发表日期：

    2025年

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  • S. S. Iyengar,
  • Seyedsina Nabavirazavi,
  • Yashas Hariprasad,
  • Prasad HB,
  • C. Krishna Mohan

摘要

This chapter delves into the use of AI and machine learning techniques to assess the likelihood of a portable executable (PE) file being malicious. It begins with an exploration of PE files, including their sections and structural components, followed by a discussion on the methodologies used to analyze these files for potential malicious behavior. Additionally, it covers the classification of malware and the strategies employed for this purpose. The core of the chapter is centered around training a machine learning model for malware detection, with an in-depth analysis of GANs, their architecture, and the specific variants used in this study. The chapter concludes by presenting the results from implementing GANs, highlighting their effectiveness and the broader implications for strengthening cybersecurity.