Semi-supervised Detection

Semi-Supervised Detection is a method of incident detection that belongs to the class of semi-supervised learning approaches, distinguished by its utilization of a small set of labeled instances alongside a large pool of unlabeled data to identify anomalies or security events while minimizing reliance on costly labeled training samples.

552

Publications

28.1K

Citations

2.1K

Authors

702

Institutions

Publications per year

2017–2026

437

Authors

2.1K

  1. KC
    Kai Chen

    Hong Kong University of Science and Technology, Hong Kong SAR China

    4 publications · 200 citations

    4

    h-index

  2. KS
    Kamal Singh

    Laboratoire Hubert Curien, France

    4 publications · 220 citations

    4

    h-index

  3. DH
    Dongxiao He

    Tianjin University, China

    4 publications · 253 citations

    4

    h-index

  4. DJ
    Di Jin

    Tianjin University, China

    4 publications · 253 citations

    4

    h-index

  5. WZ
    Wei Zhang

    Fudan University, China

    3 publications · 157 citations

    3

    h-index

1–5 of 2.1K

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 1.2K citations

    19

    h-index

  2. IEEE Access

    20 publications · 955 citations

    17

    h-index

  3. Sensors

    14 publications · 452 citations

    13

    h-index

  4. 11

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 464 citations

    10

    h-index

1–5