Data-Correlation-Aware Unsupervised Deep-Learning Model for Anomaly Detection in Cyber–Physical Systems

Abstract

A cyber–physical system (CPS) is a multidimensional complex system integrating computing, communication, and control technologies. Because of their key functionality within the system, CPS requires large robustness and security to ensure its reliable operation. Due to its importance in supporting overall system security, anomaly detection (AD) is likely to continue to play an important role in the CPS security. Moreover, unsupervised AD models based on deep learning have shown better performances in rule training, adaptive update, detection efficiency, and accuracy. Due to the nature of the CPS systems, CPS data is more likely to exhibit implicit correlative relationship among data, which would be vital to exploit for CPS security provisions in more complex data environments. In view of this observation, we propose the data-correlation-aware unsupervised deep learning model for AD in CPS, which uses an undigraph structure to store samples and implicit correlation among samples. We design a dual-autoencoder to train both original features and implicit correlation features among data, and we construct an estimation network using Gaussian mixture model (GMM) to evaluate the probability distribution of samples to complete the anomaly analysis. Experimental results compared the performance with relevant AD models based on deep learning which did not use data-correlation analysis. The results showed that, under some representative application scenarios of CPS considered, data-correlation-aware unsupervised deep-learning model achieved superior results in parameter sensitivity, ablation, relationship between correlation degree and detection performance, visualization, and detection effects.