Anomaly Detection in Multidimensional Time Series Based on Dual Adversarial Learning
Unsupervised detection of outliers in time series was a challenging problem,and the model was required to find outliers quickly and accurately.The VAE deep neural network model could learn the char-acteristics of data in data compression and recovery,due to the lack of confrontation in the training process,it couldn't better distinguish the characteristics of normal data and abnormal data,which made the model training difficult.To solve this problem,this paper proposed an improved multidimensional time series anomaly detection method based on the idea of dual confrontation.Firstly,the data set was divided into sequences of appropriate length by using a sliding window,and the model was trained using normal sequence data.Then,the dual structure was used to strengthen the confrontation between the two sets of encoders and decoders,so as to better learn the characteristics of normal data and reduce the difficulty of training.Finally,the test data containing abnormal data was put into the trained model.According to the anomaly score of the sequence to be tested in the model,the anomaly judgment was made in combination with threshold technology.Abnormal sequence fragments were obtained from the data to be tested,and the evaluation index was calculated.Experiments show that the proposed method of Dual-AE has the characteristics of easy training and strong stability,comparing with USAD method,and the F1 score and recall rate are increased by 0.01 and 0.01 on the hydrological dataset SWaT,and on the WADI dataset,the F1 score is increased by 0.09 and the recall rate is increased by 0.02.In terms of anomaly detection performance indicators,it is significantly improved in comparing with the existing generative anomaly detection models.
multidimensional time seriesencoder-decoderduality adversarial learninganomaly detection
国家科技期刊平台
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