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In the field of cybersecurity, effective malware detection remains a pivotal challenge, especially when malware is generated by AI. This paper introduces the LeqLiv-25 dataset, a comprehensive collection of labeled malware and benign representations, including raw samples and their representation such as grey images, n-grams (2-gram and 3-gram), behaviour reports and assembly code. This paper examines the fundamental principles of malware detection, with a focus on the Portable Executable (PE) file format, static analysis, dynamic analysis and the application of supervised machine learning models. Additionally, the diverse presentations of malware - grey images, n-grams, behavior reports, and assembly code - offer unique perspectives for representing and understanding malicious behavior. LeqLiv-25 stands as a valuable resource, fostering advancements in malware detection methodologies and contributing to the ongoing efforts to improve cyber defence against evolving cyber threats.

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LeqLiv-25: A Ready-to-Use Malware/Benign Dataset for Training Supervised Machine Learning Models

  • 会议论文

  • 发表日期：

    2026年

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  • Viet Hung Nguyen,
  • Linh Vo Khuong,
  • Do Nhuan Duong,
  • Phuong Thao Khong,
  • Viet Anh Phan,
  • Nathan Shone

摘要

In the field of cybersecurity, effective malware detection remains a pivotal challenge, especially when malware is generated by AI. This paper introduces the LeqLiv-25 dataset, a comprehensive collection of labeled malware and benign representations, including raw samples and their representation such as grey images, n-grams (2-gram and 3-gram), behaviour reports and assembly code. This paper examines the fundamental principles of malware detection, with a focus on the Portable Executable (PE) file format, static analysis, dynamic analysis and the application of supervised machine learning models. Additionally, the diverse presentations of malware - grey images, n-grams, behavior reports, and assembly code - offer unique perspectives for representing and understanding malicious behavior. LeqLiv-25 stands as a valuable resource, fostering advancements in malware detection methodologies and contributing to the ongoing efforts to improve cyber defence against evolving cyber threats.