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Attacks on IoT: Side-Channel Power Acquisition Framework for Intrusion Detection

Author

Listed:
  • Dominic Lightbody

    (Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland)

  • Duc-Minh Ngo

    (Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland)

  • Andriy Temko

    (Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland)

  • Colin C. Murphy

    (Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland)

  • Emanuel Popovici

    (Electrical and Electronic Engineering, University College Cork, T12 K8AF Cork, Ireland)

Abstract

This study proposes the wider use of non-intrusive side-channel power data in cybersecurity for intrusion detection. An in-depth analysis of side-channel IoT power behaviour is performed on two well-known IoT devices—a Raspberry Pi 3 model B and a DragonBoard 410c—operating under normal conditions and under attack. Attacks from the categories of reconnaissance, brute force and denial of service are applied, and the side-channel power data of the IoT testbeds are then studied in detail. These attacks are used together to further compromise the IoT testbeds in a “capture-the-flag scenario”, where the attacker aims to infiltrate the device and retrieve a secret file. Some clear similarities in the side-channel power signatures of these attacks can be seen across the two devices. Furthermore, using the knowledge gained from studying the features of these attacks individually and the signatures witnessed in the “capture the flag scenario”, we show that security teams can reverse engineer attacks applied to their system to achieve a much greater understanding of the events that occurred during a breach. While this study presents behaviour signatures analysed visually, the acquired power series datasets will be instrumental for future human-centred AI-assisted intrusion detection.

Suggested Citation

  • Dominic Lightbody & Duc-Minh Ngo & Andriy Temko & Colin C. Murphy & Emanuel Popovici, 2023. "Attacks on IoT: Side-Channel Power Acquisition Framework for Intrusion Detection," Future Internet, MDPI, vol. 15(5), pages 1-27, May.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:5:p:187-:d:1152009
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    References listed on IDEAS

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    1. Amit Sagu & Nasib Singh Gill & Preeti Gulia & Pradeep Kumar Singh & Wei-Chiang Hong, 2023. "Design of Metaheuristic Optimization Algorithms for Deep Learning Model for Secure IoT Environment," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
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    Cited by:

    1. Dominic Lightbody & Duc-Minh Ngo & Andriy Temko & Colin C. Murphy & Emanuel Popovici, 2024. "Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection," Future Internet, MDPI, vol. 16(3), pages 1-38, March.
    2. Christoph Stach & Clémentine Gritti, 2023. "Special Issue on Security and Privacy in Blockchains and the IoT Volume II," Future Internet, MDPI, vol. 15(8), pages 1-7, August.

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