Towards Systematically Deriving Defence Mechanisms from Functional Requirements of Cyber-Physical Systems

Yoong, Cheah Huei; Palleti, Venkata Reddy; Silva, Arlindo; Poskitt, Christopher M.

doi:10.1145/3384941.3409589

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Cyber-physical systems (CPSs), in which software components are deeply intertwined with physical processes, are ubiquitous in critical public infrastructure. The potential disruption that could result from a compromised system has motivated research into a multitude of CPS attack detection mechanisms, including techniques based on invariant checking [1,2,3,4], attestation [5,6,7], and fingerprinting [8,9]. A particularly popular solution is to build anomaly detectors [10,11,12,13,14,15,16], in which an underlying machine learning (ML) model is trained on a time series of the system's physical data in order to judge when future values are deviating from the norm.…”

Section: Introductionmentioning

confidence: 99%

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems

Jia,

Wang,

Poskitt

et al. 2021

Preprint

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The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers (or invariant checkers). In this work, we present an adversarial attack that simultaneously evades the anomaly detectors and rule checkers of a CPS. Inspired by existing gradient-based approaches, our adversarial attack crafts noise over the sensor and actuator values, then uses a genetic algorithm to optimise the latter, ensuring that the neural network and the rule checking system are both deceived. We implemented our approach for two real-world critical infrastructure testbeds, successfully reducing the classification accuracy of their detectors by over 50% on average, while simultaneously avoiding detection by rule checkers. Finally, we explore whether these attacks can be mitigated by training the detectors on adversarial samples.

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Section: Introductionmentioning

confidence: 99%

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems

Jia,

Wang,

Poskitt

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Deriving invariant checkers for critical infrastructure using axiomatic design principles

et al. 2021

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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.

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Towards Systematically Deriving Defence Mechanisms from Functional Requirements of Cyber-Physical Systems

Cited by 2 publications

References 36 publications

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems

Deriving invariant checkers for critical infrastructure using axiomatic design principles

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