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Extended Abstract - Transformers: Intrusion Detection Data in Disguise

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Computer Security (CyberICPS 2020, SECPRE 2020, ADIoT 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12501))

Abstract

IoT cyber security deficiencies are an increasing concern for users, operators, and developers. With no immediate and holistic device-level fixes in sight, alternative wraparound defensive measures are required. Intrusion Detection Systems (IDS) present one such option, and represent an active field of research within the IoT space. IoT environments offer rich contextual and situational information from their interaction with the physical processes they control, which may be of use to such IDS. This paper uses a comprehensive analysis of the current state-of-the-art in context and situationally aware IoT IDS to define the often misunderstood concepts of context and situational awareness in relation to their use within IoT IDS. Building on this, a unified approach to transforming and exploiting such a rich additional data set is proposed to enhance the efficacy of current IDS approaches.

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Authors and Affiliations

  1. Lancaster University, Lancaster, UK

    James Boorman, Benjamin Green & Daniel Prince

Authors
  1. James Boorman
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  2. Benjamin Green
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  3. Daniel Prince
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Corresponding authors

Correspondence to Benjamin Green or Daniel Prince .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Gjøvik, Norway

    Sokratis Katsikas

  2. Polytechnique Montréal, Montréal, QC, Canada

    Frédéric Cuppens

  3. Polytechnique Montréal, Montréal, QC, Canada

    Nora Cuppens

  4. University of Piraeus, Piraeus, Greece

    Costas Lambrinoudakis

  5. University of the Aegean, Mytilene, Greece

    Christos Kalloniatis

  6. Department of Computer Science, University of Toronto, Toronto, ON, Canada

    John Mylopoulos

  7. Georgia Institute of Technology, Atlanta, GA, USA

    Annie Antón

  8. University of Piraeus, Piraeus, Greece

    Stefanos Gritzalis

  9. Technical University of Denmark, Lyngby, Denmark

    Weizhi Meng

  10. University of Nottingham, Nottingham, UK

    Steven Furnell

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Boorman, J., Green, B., Prince, D. (2020). Extended Abstract - Transformers: Intrusion Detection Data in Disguise. In: Katsikas, S., et al. Computer Security. CyberICPS SECPRE ADIoT 2020 2020 2020. Lecture Notes in Computer Science(), vol 12501. Springer, Cham. https://doi.org/10.1007/978-3-030-64330-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-64330-0_16

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  • Online ISBN: 978-3-030-64330-0

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