Fuzzing-based Exploit Discovery

Fuzzing-Based Exploit Discovery is a vulnerability detection methodology that employs automated, randomized input generation and mutation techniques to systematically probe software systems for anomalous behaviors indicative of security flaws, distinguishing itself through its reliance on iterative fuzzing processes to uncover exploit opportunities without requiring prior knowledge of internal program logic.

14

Publications

425

Citations

58

Authors

26

Institutions

Publications per year

2017–2026

11

Authors

58

  1. SH
    Shih-Kun Huang

    National Yang Ming Chiao Tung University, Taiwan

    2 publications · 77 citations

    2

    h-index

  2. YW
    Yizhuo Wang

    National University of Singapore, Singapore

    1 publications · 67 citations

    1

    h-index

  3. JL
    Juanru Li

    Shanghai Jiao Tong University, China

    1 publications · 67 citations

    1

    h-index

  4. YW
    Yuhang Wu

    University of Houston, United States

    1 publications · 36 citations

    1

    h-index

  5. MS
    Michael Schwarz

    Graz University of Technology, Austria

    1 publications · 16 citations

    1

    h-index

1–5 of 58

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 27 citations

    2

    h-index

  2. 2022 IEEE Symposium on Security and Privacy (SP)

    Conference · 1 publications · 36 citations

    1

    h-index

  3. Neurocomputing

    1 publications · 18 citations

    1

    h-index

  4. TUbilio (Technical University of Darmstadt)

    Repository · 1 publications · 24 citations

    1

    h-index

  5. IEEE Communications Magazine

    1 publications · 15 citations

    1

    h-index

1–5