Gated recurrent unit-based parallel network traffic anomaly detection using subagging ensembles

Recently, wireless network evolution has been primarily driven by a need for higher rates. The ongoing deployment of 5G cellular systems is continuously exposing the inherent limitations of this system, which promote the exploration of 6th generation mobile networks (6G). However, development is bound to be challenging. The complex network environment, rapidly growing data volume and new types of network attacks and anomalies will become an obstacle to network security protection. To solve these problems, we propose a novel parallel subagging-GRU-based network traffic anomaly detection method (PSB-GRU) for identifying anomalies in the network. Considering the advantages of gated recurrent unit (GRU) self-learning and long-term dependency processing, we use it as the main structure of anomaly detection, and we use a genetic algorithm (GA) to realize the intelligentization of its training process. In addition, we introduce the Spark platform to parallelize the detection process and improve detection efficiency. Additionally, to reduce the variance and mean square error in all order terms and improve the generalization ability of the detection model, we utilize a subagging algorithm to reinforce the detection model. Finally, we compare our anomaly detection method with some existing algorithms and show that the anomaly detection performance of the proposed method is better than that of the recurrent neural network methods (RNN, LSTM and GRU).