Abstract: Traditional time series anomaly detection models face challenges in accurately extracting temporal relationships among multivariate sensor and actuator data in Cyber-Physical Systems (CPS), impacting the performance of anomaly detection. To address this issue, this paper proposes a novel time series anomaly detection method named Auto-Correlation-Variational Autoencoder-Generative Adversarial Network (AM-VAE-GAN, abbreviated as AMVG). Built upon GAN, the method utilizes NOISE data augmentation to expand the training dataset. By introducing auto-correlation matrices to enhance data dependencies and combining the data reconstruction capability of variational autoencoders, the model's robustness is strengthened, leading to further improvements in anomaly detection performance. The two decoders of AMVG form mutually antagonistic G and D networks, engaging in continuous adversarial training to optimize the model's detection capability. Experimental validation on three real-world CPS datasets demonstrates that the AMVG method achieves significant improvements in accuracy, recall, and F1 scores compared to state-of-the-art methods. Specifically, the F1 scores on the three datasets are 0.953, 0.758, and 0.891, with respective increases of 6.2%, 3.4%, and 7.5%. These results underscore the accuracy and effectiveness of the proposed method in CPS anomaly detection.

摘要： 传统时间序列异常检测模型在处理信息物理系统（CPS）中的多元传感器和执行器数据时，难以准确提取多元数据之间的时序联系，从而影响异常检测性能。为解决这一问题，本文提出一种新的时间序列异常检测方法，称为自相关-变分自编码-对抗学习网络（AM-VAE-GAN，简称AMVG）。该方法以GAN为基础，使用NOISE数据增强方法扩展训练数据量，并通过引入自相关矩阵增强数据依赖关系，结合变分自编码器的数据重建能力，加强模型鲁棒性同时进一步提高异常检测模型性能，其中AMVG的两个解码器构成互相对抗的G网络和D网络，G和D不断对抗训练优化模型的检测能力。通过在三个真实世界的CPS数据集上进行实验验证，AMVG方法相较于最新研究方法在精度、召回率、以及F1分数等综合性能上均取得显著提高。具体而言， AMVG在三个数据集上的F1分数分别为0.953、0.758、0.891，其中F1值分别提高了6.2%、3.4%、7.5%，这表明该方法在CPS异常检测中的准确性和有效性。