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Data-centric approaches are becoming increasingly common in the creation of defense mechanisms for critical infrastructure such as the electric power grid and water treatment plants. Such approaches often use well-known methods from machine learning and system identification, i.e., the Multi-Layer Perceptron, Convolutional Neural Network, and Deep Auto Encoders to create process anomaly detectors. Such detectors are then evaluated using data generated from an operational plant or a simulator; rarely is the assessment conducted in real time on a live plant. Regardless of the method to create an anomaly detector, and the data used for performance evaluation, there remain significant challenges that ought to be overcome before such detectors can be deployed with confidence in city-scale plants or large electric power grids. This position paper enumerates such challenges that the authors have faced when creating data-centric anomaly detectors and using them in a live plant. CCS CONCEPTS • Security and privacy → Intrusion/anomaly detection; • Computer systems organization → Sensors and actuators; Embedded systems; Dependable and fault-tolerant systems and networks.

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Abstract. We present an attack detection scheme for a water treatment system. We leverage the connectivity of two stages of the process to detect attacks downstream from the point of attack. Based on a mathematical model of the process, carefully crafted and executed attacks, are detected by deploying CUSUM and Bad-Data detectors. Extensive experiments are carried out and the results show the performance of the proposed scheme.

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