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Deriving invariant checkers for critical infrastructure using axiomatic design principles

Cheah Huei Yoong, Venkata Reddy Palleti, Rajib Ranjan Maiti, , Christopher M Poskitt
Published: 2 April 2021
Cybersecurity , Volume 4, pp 1-24; doi:10.1186/s42400-021-00069-7

Abstract: Cyber-physical systems (CPSs) in critical infrastructure face serious threats of attack, motivating research into a wide variety of defence mechanisms such as those that monitor for violations of invariants, i.e. logical properties over sensor and actuator states that should always be true. Many approaches for identifying invariants attempt to do so automatically, typically using data logs, but these can miss valid system properties if relevant behaviours are not well-represented in the data. Furthermore, as the CPS is already built, resolving any design flaws or weak points identified through this process is costly. In this paper, we propose a systematic method for deriving invariants from an analysis of a CPS design, based on principles of the axiomatic design methodology from design science. Our method iteratively decomposes a high-level CPS design to identify sets of dependent design parameters (i.e. sensors and actuators), allowing for invariants and invariant checkers to be derived in parallel to the implementation of the system. We apply our method to the designs of two CPS testbeds, SWaT and WADI, deriving a suite of invariant checkers that are able to detect a variety of single- and multi-stage attacks without any false positives. Finally, we reflect on the strengths and weaknesses of our approach, how it can be complemented by other defence mechanisms, and how it could help engineers to identify and resolve weak points in a design before the controllers of a CPS are implemented.
Keywords: Cyber-physical systems / Critical infrastructure / Industrial control systems / Systematic design framework / Axiomatic design / Invariants / Anomaly detection / Supervised machine learning

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