关键词: 数据异常检测, 自编码网络, 深度自编码网络, 曲线下面积（AUC）

Abstract:

Normal data for training is usually required in Auto Encoder（AE） network, which limits its applications in data anomaly detection. This paper proposes an unsupervised data anomaly detection method based on a Deep Auto Encoder（DAE） network model. In this model, Principal Components Analysis（PCA） is introduced, and the anomaly data is isolated by differencing each reconstruction output of AE and the input data. That is, the input data is divided into normal data and anomaly data, where the normal data is reconstructed via the AE network, and the anomaly data is optimized before outputting. Finally, the whole model is trained by the Alternating Direction Method of Multipliers（ADMM）, and the results are outputted when the predetermined number of training times is sucessfully achieved. The DAE model is compared with eight machine learning models and AE model in seven real datasets. The results show that the DAE model can effectively carry out model training without inputting normal data and prevent model from overfitting, and the overall performances are better than those using the traditional machine learning model and AE model. The AUC values of DAE model are optimal in 4 datasets, among which, the AUC value obtained from the DAE model is 10.93% higher than that from the Isolated Forest（IF） method in the mnist datasets.

Key words: data anomaly detection, auto encoder network, Deep Auto Encoder network（DAE）, Area Under the Curve（AUC）