GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems

• URL: http://arxiv.org/abs/2504.20906v1
• Date: Tue, 29 Apr 2025 16:24:11 GMT
• Title: GiBy: A Giant-Step Baby-Step Classifier For Anomaly Detection In Industrial Control Systems
• Authors: Sarad Venugopalan, Sridhar Adepu,
• Abstract summary: We propose an anomaly detection method that involves accurate linearization of the non-linear forms arising from sensor-actuator(s) relationships.<n>We accomplish this by using a well-known water treatment testbed as a use case.
• Score: 1.450261153230204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The continuous monitoring of the interactions between cyber-physical components of any industrial control system (ICS) is required to secure automation of the system controls, and to guarantee plant processes are fail-safe and remain in an acceptably safe state. Safety is achieved by managing actuation (where electric signals are used to trigger physical movement), dependent on corresponding sensor readings; used as ground truth in decision making. Timely detection of anomalies (attacks, faults and unascertained states) in ICSs is crucial for the safe running of a plant, the safety of its personnel, and for the safe provision of any services provided. We propose an anomaly detection method that involves accurate linearization of the non-linear forms arising from sensor-actuator(s) relationships, primarily because solving linear models is easier and well understood. Further, the time complexity of the anomaly detection scenario/problem at hand is lowered using dimensionality reduction of the actuator(s) in relationship with a sensor. We accomplish this by using a well-known water treatment testbed as a use case. Our experiments show millisecond time response to detect anomalies and provide explainability; that are not simultaneously achieved by other state of the art AI/ML models with eXplainable AI (XAI) used for the same purpose. Further, we pin-point the sensor(s) and its actuation state for which anomaly was detected.

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