Deep Learning-based Malware Detection

Deep Learning-Based Malware Detection is a computational methodology within the field of malware analysis that employs deep neural networks to automatically identify and classify malicious software by learning hierarchical representations of raw or processed program data, distinguishing itself from traditional detection techniques through its reliance on data-driven feature extraction and complex nonlinear modeling.

783

Publications

53.5K

Citations

2.4K

Authors

806

Institutions

Publications per year

2017–2026

757

Authors

2.4K

  1. MA
    Mamoun Alazab

    Charles Darwin University, Australia

    12 publications · 2.1K citations

    12

    h-index

  2. RV
    R. Vinayakumar

    Amrita Vishwa Vidyapeetham, India

    11 publications · 1.4K citations

    11

    h-index

  3. PP
    Prabaharan Poornachandran

    Amrita Vishwa Vidyapeetham, India

    10 publications · 1.1K citations

    10

    h-index

  4. FU
    Farhan Ullah

    Northwestern Polytechnical University, China

    9 publications · 714 citations

    9

    h-index

  5. VR
    Vinayakumar Ravi

    Prince Mohammad bin Fahd University, Saudi Arabia

    9 publications · 591 citations

    9

    h-index

1–5 of 2.4K

Venues

  1. Computers & Security

    45 publications · 4.5K citations

    36

    h-index

  2. IEEE Access

    45 publications · 4.1K citations

    33

    h-index

  3. arXiv (Cornell University)

    Repository · 42 publications · 2.4K citations

    23

    h-index

  4. Applied Sciences

    16 publications · 578 citations

    16

    h-index

  5. Security and Communication Networks

    14 publications · 615 citations

    14

    h-index

1–5