Demise: Interpretable deep extraction and mutual information selection techniques for IoT intrusion detection

Luke R. Parker, Paul D. Yoo*, Taufiq A. Asyhari, Lounis Chermak, Yoonchan Jhi, Kamal Taha

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    28 Citations (SciVal)
    Original languageEnglish
    Title of host publicationProceedings of the 14th International Conference on Availability, Reliability and Security, ARES 2019
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450371643
    DOIs
    Publication statusPublished (VoR) - 26 Aug 2019
    Event14th International Conference on Availability, Reliability and Security, ARES 2019 - Canterbury, United Kingdom
    Duration: 26 Aug 201929 Aug 2019

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference14th International Conference on Availability, Reliability and Security, ARES 2019
    Country/TerritoryUnited Kingdom
    CityCanterbury
    Period26/08/1929/08/19

    Keywords

    • Deep learning
    • Feature engineering
    • IoT
    • Lightweight intrusion detection
    • Mutual information
    • Security mobility applications
    • Security of resource constrained devices
    • White-box modelling

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