Computer Science ›› 2023, Vol. 50 ›› Issue (5): 355-362.doi: 10.11896/jsjkx.220400221

• Information Security • Previous Articles     Next Articles

Multimodal Generative Adversarial Networks Based Multivariate Time Series Anomaly Detection

ZHANG Renbin1,2, ZUO Yicong1, ZHOU Zelin1, WANG Long1, CUI Yuhang1   

  1. 1 School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China
    2 Anhui Province Key Laboratory of Industry Safety and Emergency Technology,Hefei 230601,China
  • Received:2022-04-22 Revised:2022-09-11 Online:2023-05-15 Published:2023-05-06
  • About author:ZHANG Renbin,born in 1971,Ph.D,associate professor.His main research interests include industrial Internet secu-rity and artificial intelligence.
  • Supported by:
    National Key Research and Development Projects of China(2016YFC0801804,2016YFC0801405) and Fundamental Research Funds for the Central Universities of China(PA2019GDPK0074).

Abstract: Aiming at the problem that the traditional anomaly detection model of multivariate time series data does not consider the multimodal distribution of spatio-temporal data,a multivariate time series data anomaly detection model based on multimodal generative adversarial networks is proposed.The sliding windows is used to segment the time series and construct feature matrices,so as to capture the multimodal features of the data.Feature matrix and raw data are fed into the multimodal encoder and multimodal generator as modal information respectively,then multimodal feature matrix with spatio-temporal information is outputted.The real data is encoded into feature matrices and the two types of feature matrices are utilized as discriminator inputs.In the proposed method,a gradient penalty method and the Wasserstein distance between the real and generated distributions to replace the binary cross-entropy loss are utilized to train the discriminator,then combining the generator reconstruction error and discriminator scores to detect anomalies.Experimental results based on the secure water treatment(SWaT) and the water distribution(WADI) datasets show that,compared with the baseline model,the proposed method improves the F1-score metrics by 0.11 and 0.19 respectively.The proposed method can identify multivariate time series data anomalies well,with good robustness and generalizability.

Key words: Multivariate time series, Anomaly detection, Semi-supervised learning, Adversarial learning, Multimodal

CLC Number: 

  • TP311.13
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