Anomaly Detection of Network Traffic Based on Prediction and Self-Adaptive Threshold

Security problems with network are significant, such as network failures and malicious attacks. Monitoring network traffic and detect anomalies of network traffic is one of the effective manner to ensure network security. In this paper, we propose a hybrid method for network traffic prediction and anomaly detection. Specifically, the original network traffic data is decomposed into high-frequency components and low-frequency components. Then, non-linear model Relevance Vector Machine (RVM) model and ARMA (Auto Regressive Moving Average) model are employed respectively for prediction. After combining the prediction, a self-adaptive threshold method based on Central Limit Theorem (LCT) is introduced for anomaly detection. Moreover, our extensive experiments evaluate the efficiency of proposed method.