With the proliferation of data complexity, traditional methods for anomaly detection are no longer applicable, while deep learning can process complex data and perform anomaly detection by multi-layer neural network. Through extensive research on domestic and international research results, firstly, anomaly detection and the types of detection methods are introduced. And deep learning based anomaly detection models are classified into three categories of generative-based model, reconstruction-based model and single classification-based model. Secondly,the construction principles and development context of deep anomaly detection models under different categories are sorted out, and the applicability, advantages and disadvantages of various models are analyzed. Then, the commonly used datasets and evaluation indicators for anomaly detection are introduced, and the performance of different models are compared and analyzed. Finally, the challenges faced by deep anomaly detection are summarized and the future research directions are prospected.
