TranBF: Deep Transformer Networks and Bayesian Filtering for Time Series Anomalous Signal Detection in Cyber-physical Systems

Effective anomalous signal detection in time series multimedia data is imperative for safety-critical cyber-physical systems (CPS). Nevertheless, constructing a system for precise and rapid anomaly detection is challenging due to complex system dynamics, long-range dependencies, and unidentified sensor noise in modern CPS. This study proposes TranBF, an innovative time series anomalous signal detection method designed with a carefully engineered deep Transformer network and Bayesian Filtering. TranBF aims to capture the dynamics and broader temporal dependencies of CPS within a dynamic state-space and recursively track the uncertainty of system noise over time, thereby significantly improving the robustness and accuracy of anomaly detection. Extensive experiments on three real-world public datasets demonstrate that TranBF can significantly out-perform state-of-the-art baseline methods in terms of detection performance. Specifically, TranBF enhances F1 scores by a maximum of 16.5% while concurrently reducing training times by as much as 39.3% compared to the baseline models. Furthermore, the ablation study furnishes empirical evidence supporting the effectiveness of each component within TranBF.