Error Bounds for Identification of a Class of Continuous LTI Systems

Polisetty, Venkata Goutham; Varanasi, Santhosh Kumar; Jampana, Phanindra

doi:10.1016/j.ifacol.2019.06.098

Cited by 2 publications

(2 citation statements)

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“…Given a certain operating range, several control systems can be approximated with high accuracy by an LTI model using well-established techniques. This modelling framework is especially applicable to several problems in the process control industry [46].…”

Section: Framework Configuration Requirementsmentioning

confidence: 99%

Grounds for Suspicion: Physics-Based Early Warnings for Stealthy Attacks on Industrial Control Systems

Azzam

Pasquale

Provan

et al. 2022

IEEE Trans. Dependable and Secure Comput.

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Stealthy attacks on Industrial Control Systems can cause significant damage while evading detection. In this paper, instead of focusing on the detection of stealthy attacks, we aim to provide early warnings to operators, in order to avoid physical damage and preserve in advance data that may serve as an evidence during an investigation. We propose a framework to provide grounds for suspicion, i.e. preliminary indicators reflecting the likelihood of success of a stealthy attack. We propose two grounds for suspicion based on the behaviour of the physical process: (i) feasibility of a stealthy attack, and (ii) proximity to unsafe operating regions. We propose a metric to measure grounds for suspicion in real-time and provide soundness principles to ensure that such a metric is consistent with the grounds for suspicion. We apply our framework to Linear Time-Invariant (LTI) systems and formulate the suspicion metric computation as a real-time reachability problem. We validate our framework on a case study involving the benchmark Tennessee-Eastman process. We show through numerical simulation that we can provide early warnings well before a potential stealthy attack can cause damage, while incurring minimal load on the network. Finally, we apply our framework on a use case to illustrate its usefulness in supporting early evidence collection.

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Section: Framework Configuration Requirementsmentioning

confidence: 99%