Adaptive-Correlation-Aware Unsupervised Deep Learning for Anomaly Detection in Cyber-Physical Systems

Cyber-Physical System needs high security to ensure the safe operation. Anomaly detection is one of the mainstream security technologies, the core of which is data analysis and learning. Unsupervised Deep-Learning-based Anomaly Detection Methods can be used in the scenarios that collects large amounts of unlabeled data and are more in line with the actual needs of CPS. However, the correlation among data did not attract enough attention to exploring their implicit relationship, and the adaptive training was deficient. Therefore, we propose an
A
daptive-
C
orrelation-aware
U
nsupervised
D
eep
L
earning (ACUDL) for anomaly detection in CPS. It constructs a directed graph structure to represent the implicit correlation among data and adaptively updates with dynamic graph; then, designs a dual-autoencoder to extract the original non-correlation, correlation, and reconstruction features, and builds an estimation network using the Gaussian mixture model (GMM) to estimate the anomaly energy. Experimental results on several CPS data scenarios show that ACUDL can be well adapted to many application scenarios with different data characteristics and achieves better overall results than some up-to-date DL-ADMs.