Sequence-based Malware Classification

Sequence-Based Malware Classification is a malware classification approach that distinguishes itself by analyzing sequential representations of code or behavior—such as instruction sequences, system call traces, or opcode streams—using pattern recognition or machine learning techniques to identify malicious characteristics based on order, structure, and temporal dependencies.

37

Publications

2.2K

Citations

129

Authors

51

Institutions

Publications per year

2017–2026

23

Authors

129

  1. LR
    Lior Rokach

    Ben-Gurion University of the Negev, Israel

    2 publications · 45 citations

    2

    h-index

  2. EG
    Eul Gyu Im

    Hanyang University, South Korea

    2 publications · 48 citations

    2

    h-index

  3. TM
    Tyler Moore

    University of Tulsa, United States

    2 publications · 128 citations

    2

    h-index

  4. JD
    Jake Drew

    Southern Methodist University, United States

    2 publications · 128 citations

    2

    h-index

  5. YE
    Yuval Elovici

    Ben-Gurion University of the Negev, Israel

    2 publications · 45 citations

    2

    h-index

1–5 of 129

Venues

  1. Computers & Security

    3 publications · 251 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 68 citations

    3

    h-index

  3. Journal in Computer Virology

    2 publications · 144 citations

    2

    h-index

  4. Journal of Information Security and Applications

    JISA · 1 publications · 27 citations

    1

    h-index

  5. 1

    h-index

1–5