Efficient Predictive Monitoring of Linear Time-Invariant Systems Under Stealthy Attacks
Mazen Azzam, Liliana Pasquale, Gregory Provan, Bashar Nuseibeh

TL;DR
This paper introduces an efficient online safety monitoring method for linear systems in industrial control, capable of early detection of stealthy attacks that evade traditional anomaly detectors, enhancing system security.
Contribution
It adapts reachability analysis for real-time safety monitoring of LTI systems under stealthy attacks, combining offline symbolic reachable set computation with online safety checks using ellipsoidal calculus.
Findings
The approach predicts attack impact timely and accurately.
It outperforms baseline methods in efficiency and scalability.
Demonstrated effectiveness on Tennessee-Eastman process.
Abstract
Attacks on Industrial Control Systems (ICS) can lead to significant physical damage. While offline safety and security assessments can provide insight into vulnerable system components, they may not account for stealthy attacks designed to evade anomaly detectors during long operational transients. In this paper, we propose a predictive online monitoring approach to check the safety of the system under potential stealthy attacks. Specifically, we adapt previous results in reachability analysis for attack impact assessment to provide an efficient algorithm for online safety monitoring for Linear Time-Invariant (LTI) systems. The proposed approach relies on an offline computation of symbolic reachable sets in terms of the estimated physical state of the system. These sets are then instantiated online, and safety checks are performed by leveraging ideas from ellipsoidal calculus. We…
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Taxonomy
TopicsSmart Grid Security and Resilience · Risk and Safety Analysis · Anomaly Detection Techniques and Applications
