Early and accurate anomaly detection in critical infrastructure (CI), such as water treatment plants and electric power grid, is necessary to avoid plant damage and service disruption. Several machine learning techniques have been employed for the design of an effective anomaly detector in such systems. However, threats such as from insiders and state actors, introduce challenges in the design of an effective anomaly detector. This work presents a multi-layer perceptron (MLP) based anomaly detector that uses an unsupervised approach to safeguard CI from the adverse impacts of cyber-attacks.
