Abnormal Traffic Detection Based on a Fusion BiGRU Neural Network

Abstract

As network security is getting more and more attention, methods for anomalous traffic detection are proposed. However, the methods for anomalous traffic detection have problems such as low detection rate and high false alarm rate, so this paper proposes a two-branch neural network based on BiGRU network as the backbone. The model uses the dual-branch and BiGRU networks to extract and analyze the spatial and temporal features of the data, and finally discriminates the abnormal traffic using a Softmax classifier. Experimental results on the ISCX-IDS-2012 dataset and CIC-IDS-2017 dataset show that the model has a low false alarm rate and high accuracy rate, which is better than existing methods (This work was supported by the Research and development project of China Electric Power Research Institute, (NO. 5242002000QT)).