Runtime Anomaly Detection

Runtime Anomaly Detection is a dynamic vulnerability analysis method that identifies deviations from established behavioral norms during program execution to detect潜在 security violations or malfunctions in real time.

123

Publications

7.7K

Citations

457

Authors

191

Institutions

Publications per year

2017–2026

66

Authors

457

  1. DY
    Danfeng Yao

    Virginia Tech, United States

    3 publications · 144 citations

    3

    h-index

  2. XG
    Xiaohui Gu

    North Carolina State University, United States

    3 publications · 108 citations

    3

    h-index

  3. AP
    Andy Podgurski

    Case Western Reserve University, United States

    2 publications · 35 citations

    2

    h-index

  4. MS
    Margo Seltzer

    Harvard University Press, United States

    2 publications · 101 citations

    2

    h-index

  5. TP
    Thomas Pasquier

    University of Cambridge, United Kingdom

    2 publications · 101 citations

    2

    h-index

1–5 of 457

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 121 citations

    5

    h-index

  2. IEEE Access

    4 publications · 258 citations

    4

    h-index

  3. Journal of Systems and Software

    JSS · 4 publications · 207 citations

    4

    h-index

  4. Future Generation Computer Systems

    FGCS · 2 publications · 81 citations

    2

    h-index

  5. ACM Transactions on Embedded Computing Systems

    TECS · 2 publications · 50 citations

    2

    h-index

1–5