Publication Type

Journal Article

Version

publishedVersion

Publication Date

12-2021

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

Discipline

Information Security

Research Areas

Cybersecurity

Publication

Cybersecurity

Volume

4

Issue

1

First Page

1

Last Page

24

ISSN

2523-3246

Identifier

10.1186/s42400-021-00069-7

Publisher

SpringerOpen

Embargo Period

8-3-2021

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1186/s42400-021-00069-7

Share

COinS