Anomaly Detection of Physical Systems Based on Autocorrelation-Variance Adversarial Learning
Traditional time-series anomaly detection models struggle to accurately extract temporal connections between the multivariate sensor and actuator data in Cyber-Physical Systems(CPS),thereby affecting anomaly detection performance.To address this issue,this paper proposes a novel time-series anomaly detection method called the autocorrelation-Variational Auto-Encoder(VAE)-adversarial learning network AMVG.Built on a Generative Adversarial Network(GAN),this method uses Noise data augmentation to expand the training dataset.By introducing autocorrelation matrices to enhance data dependencies and combining the data reconstruction capabilities of VAE,the robustness of the model is strengthened,further improving the anomaly detection performance.The two decoders of the AMVG form mutually antagonistic G and D networks,engaging in continuous adversarial training to optimize the detection capability of the model.Experimental validation on three real-world CPS datasets demonstrates that the AMVG method achieves significant improvements in accuracy,recall,and F1 value compared to state-of-the-art methods.Specifically,the F1 values for the three datasets are 0.953,0.758,and 0.891.Compared to the suboptimal USAD and GRELEN methods,the AMVG method can increase the F1 values by at least 6.2,3.4,and 7.5 percentage points,respectively.
万方数据
维普
