SSDCL: Semi-Supervised Denoising-Aware Contrastive Learning for Time Series Anomaly Detection in Cyber-Physical Systems

Time series anomaly detection is crucial for improving the security and reliability of Cyber-Physical systems (CPS). While significant progress has been made, existing methods struggle to learn discriminative representations from multivariate time series with complex interactions and noise. To address this challenge, we propose a semi-supervised anomaly detection method based on denoising-aware contrastive learning, namely SSDCL, which can achieve robust performance for CPS anomaly detection using limited supervision. Specifically, we first design a similarity combination data augmentation algorithm to handle complex interactions among continuous sensor measurements and discrete actuator states. Furthermore, we develop a denoising hierarchical contrastive loss function that mitigates data noise interference while ensuring discriminative spatio-temporal representation. To validate the effectiveness of SSDCL, we conducted empirical evaluations on three publicly available CPS time series datasets including PUMP, SWaT and WADI. The experimental results show that the proposed method achieves F1 Score of 97.5%, 93.0%, and 74.4%, respectively, outperforming the state-of-the-art (SOTA) CPS anomaly detection methods.