Curse of system complexity and virtue of operational invariants : machine learning based system modeling and attack detection in CPS

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Shahid, Muhammad Omer and Ahmed, Chuadhry Mujeeb and Palleti, Venkata Reddy and Zhou, Jianying; (2022) Curse of system complexity and virtue of operational invariants : machine learning based system modeling and attack detection in CPS. In: 2022 IEEE Conference on Dependable and Secure Computing (DSC). IEEE, GBR, pp. 1-8. ISBN 9781665421416 (https://doi.org/10.1109/dsc54232.2022.9888940)

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Abstract

Cyber Physical Systems (CPS) security has gained a lot of interest in recent years. Different approaches have been proposed to tackle the security challenges. Intrusion detection has been of most interest so far, involving design-based and data-based approaches. Design-based approaches require domain expertise and are not scalable, on the other hand, data-based approaches suffer from the lack of real-world datasets available for specific critical physical processes. In this work, a data collection effort is made on a realistic Water Distribution (WADI) test-bed. Collected data consists of both the normal operation as well as a range of attack scenarios. Next, machine learning-based system-modeling techniques are considered using the data from WADI. It is shown that the accuracy of system model-based intrusion detectors depends on the model accuracy and for non-linear processes, it is non-trivial to obtain accurate system models. Moreover, an operational invariants-based attack detection technique is proposed using the system design parameters. It is shown that using a simple rule-based anomaly detector performs better than the complex black-box data-based techniques.

ORCID iDs

Shahid, Muhammad Omer, Ahmed, Chuadhry Mujeeb ORCID logoORCID: https://orcid.org/0000-0003-3644-0465, Palleti, Venkata Reddy and Zhou, Jianying;
  • Item type:Book Section
    ID code:82643
    Dates:
    Date
    Event
    26 September 2022
    Published
    24 June 2022
    Published Online
    31 March 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:10 Oct 2022 14:11
    Last modified:12 Dec 2024 01:33
    URI:https://strathprints.strath.ac.uk/id/eprint/82643
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