Network Traffic Anomaly Detection Method Based on CAE and LSTM

Abstract

This paper constructs a deep learning method for detecting network traffic anomalies to enhance the secure transmission of data in networks due to the complex, diverse and numerous types of anomalous traffic in current networks. The method combines multiple convolutional auto-encoders (Multi-CAE) with a long short-term memory network. The convolutional auto-encoders are obtained by combining stacked auto-encoders with convolutional layers, which can not only reduce feature loss but also effectively extract the spatial structure of samples. The use of Multi-CAE greatly improves the feature extraction capability, and combined with the long short-term memory network to extract temporal features, the effective features extracted in this paper are more comprehensive and less losses compared to the models used in other researches. A comparison of the loss values in the training of CAE (Convolutional Auto-Encoders) and SAE (Stacked Auto-Encoders) in the experiments shows that the loss values of CAE are about one-tenth lower than those of SAE, and the method consisting of Multi-CAE and LSTM for the USTC- TFC2016 dataset was trained with accuracy values up to 99.98%, and the precision, recall and f1-score parameters were also above 99%, outperforming other studies.