Autoencoder-based anomaly detection in water cyber-physical systems

Abstract

Cyber-physical systems (CPS) are at the core of enabling services in the critical infrastructures, such as a smart water network, hence the need for effective anomaly detection to ensure operational safety and security. This paper proposes deep water anomaly detection (DeepWAD), a big data-based multiple purpose anomaly detection tool that leverages deep learning, supports big data, and internet of things (IoT) data collection and analysis. In a unified processing pipeline, it estimates and contrasts three kinds of autoencoders, viz adversarial
autoencoder (AAE), variational autoencoder (VAE), and classical autoencoder (CAE). These models are then applied to a practical water treatment system, namely the secure water treatment (SWaT) dataset. Using reconstruction errors, DeepWAD analyzes time-series data generated by IoT sensors in order to discover anomalies including, for example, equipment malfunctions, sensor errors, and
cyber threats. The framework also demonstrates the performance of various architectures in terms of data complexity, training consistency, and reconstruction accuracy. Due to its generality, DeepWAD can also be applied in other CPS domains such as the energy grid or industrial control systems. It serves as a decision-support tool to select the most appropriate anomaly detection model depending on system constraints and detection requirements.