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

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

International Journal of Critical Infrastructure Protection, 2020, vol. 28, issue C

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.

Keywords: Association rule mining; Critical Infrastructure; Cyber-physical attacks; Distributed attack detection; SCADA security; Machine learning; Water treatment plant (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijocip:v:28:y:2020:i:c:s1874548220300056

DOI: 10.1016/j.ijcip.2020.100341

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