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Generating invariants using design and data-centric approaches for distributed attack detection

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  • Umer, Muhammad Azmi
  • Mathur, Aditya
  • Junejo, Khurum Nazir
  • Adepu, Sridhar

Abstract

A cyber attack launched on a critical infrastructure (CI), such as a power grid or a water treatment plant, could lead to anomalous behavior. There exist several methods to detect such behavior. This paper reports on a study conducted to compare two methods for detecting anomalies in CI. One of these methods, referred to as design-centric, generates invariants from the design of a CI. Another method, referred to as data-centric, generates the invariants from data collected from an operational CI. The key question that motivated the study is “How do design and data-centric methods compare in the effectiveness of the generated invariants in detecting process anomalies.” The data-centric approach used Association Rule Mining for generating invariants from operational data. These invariants, and their performance in detecting anomalies, was compared against those generated by a design-centric approach reported in the literature. The entire study was conducted in the context of an operational scaled down version of a water treatment plant.

Suggested Citation

  • Umer, Muhammad Azmi & Mathur, Aditya & Junejo, Khurum Nazir & Adepu, Sridhar, 2020. "Generating invariants using design and data-centric approaches for distributed attack detection," International Journal of Critical Infrastructure Protection, Elsevier, vol. 28(C).
    • Handle: RePEc:eee:ijocip:v:28:y:2020:i:c:s1874548220300056
    DOI: 10.1016/j.ijcip.2020.100341
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    Cited by:

    1. Umer, Muhammad Azmi & Junejo, Khurum Nazir & Jilani, Muhammad Taha & Mathur, Aditya P., 2022. "Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).

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