Unsupervised intrusion detection system for unmanned aerial vehicle with less labeling effort

Kyung Ho Park, Eunji Park, Huy Kang Kim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    14 Citations (Scopus)

    Abstract

    Along with the importance of safety, an IDS has become a significant task in the real world. Prior studies proposed various intrusion detection models for the UAV. Past rule-based approaches provided a concrete baseline IDS model, and the machine learning-based method achieved a precise intrusion detection performance on the UAV with supervised learning models. However, previous methods have room for improvement to be implemented in the real world. Prior methods required a large labeling effort on the dataset, and the model could not identify attacks that were not trained before. To jump over these hurdles, we propose an IDS with unsupervised learning. As unsupervised learning does not require labeling, our model let the practitioner not to label every type of attack from the flight data. Moreover, the model can identify an abnormal status of the UAV regardless of the type of attack. We trained an autoencoder with the benign flight data only and checked the model provides a different reconstruction loss at the benign flight and the flight under attack. We discovered that the model produces much higher reconstruction loss with the flight under attack than the benign flight; thus, this reconstruction loss can be utilized to recognize an intrusion to the UAV. With consideration of the computation overhead and the detection performance in the wild, we expect our model can be a concrete and practical baseline IDS on the UAV.

    Original languageEnglish
    Title of host publicationInformation Security Applications - 21st International Conference, WISA 2020, Revised Selected Papers
    EditorsIlsun You
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages45-58
    Number of pages14
    ISBN (Print)9783030652982
    DOIs
    Publication statusPublished - 2020
    Event21st International Conference on Information Security Applications, WISA 2020 - Jeju Island, Korea, Republic of
    Duration: 2020 Aug 262020 Aug 28

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume12583 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference21st International Conference on Information Security Applications, WISA 2020
    Country/TerritoryKorea, Republic of
    CityJeju Island
    Period20/8/2620/8/28

    Bibliographical note

    Publisher Copyright:
    © Springer Nature Switzerland AG 2020.

    Keywords

    • Autoencoder
    • Intrusion detection system
    • Unmanned aerial vehicle
    • Unsupervised learning

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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