Machine Learning For Vulnerability Detection

Machine Learning for Vulnerability Detection is a computational methodology within static application security testing that employs machine learning models to automatically identify software vulnerabilities by learning patterns from historical codebases and vulnerability datasets.

310

Publications

16.1K

Citations

1K

Authors

416

Institutions

Publications per year

2017–2026

275

Authors

1K

  1. YL
    Yang Liu

    Nanyang Technological University, Singapore

    6 publications · 285 citations

    6

    h-index

  2. DL
    David Lo

    Singapore Management University, Singapore

    5 publications · 207 citations

    5

    h-index

  3. CH
    Cheng Huang

    Sichuan University, China

    5 publications · 208 citations

    5

    h-index

  4. NN
    Nuno Neves

    University of Lisbon, Portugal

    4 publications · 329 citations

    4

    h-index

  5. YX
    Yang Xiang

    Deakin University, Australia

    4 publications · 756 citations

    4

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 411 citations

    11

    h-index

  2. IEEE Access

    11 publications · 552 citations

    11

    h-index

  3. Applied Sciences

    8 publications · 189 citations

    8

    h-index

  4. Computers & Security

    9 publications · 248 citations

    8

    h-index

  5. Journal of Systems and Software

    JSS · 6 publications · 391 citations

    6

    h-index

1–5