Network traffic anomaly detection method based on chaotic neural network

Abstract

Network abnormal traffic detection is a hot topic in network security. Based on the theory of chaotic neural network, this paper constructs a network traffic anomaly detection model to solve the problems of high dimension of abnormal traffic and overfitting of classification model caused by outliers. The model firstly calculates mutual information according to the correlation degree between features and class tags, and selects excellent feature subset to extract suitable features for online traffic anomaly detection. Then, chaotic neural network algorithm is used to search the best feature subset from the original feature set, and an adaptive strategy is adopted to add and delete the iterated features. The chaotic neural network is used to specify the solution strategy for the problem that it is difficult to select the candidate feature subset as the information gain of each feature in the traffic record, which contributes to improving the quality of data. The results show that this method can reduce the training time by more than 12% without losing the performance indexes of the original classification model, and significantly improve the classification performance and prediction accuracy.