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Network traffic anomaly detection methods can detect traffic that is significantly different from normal traffic by analyzing network traffic, and are seen as an effective means to detect unknown new attacks because they do not rely on static feature codes. However, most of the current network traffic anomaly detection methods have low accuracy and high false alarm rate. In this paper, an OS-ELM (Online Sequential Extreme Learning Machine) network traffic anomaly detection method is proposed. First, we use SMOTE to balance the data samples to improve the classification detection performance. In order to reduce the high dimensionality between data features and eliminate the redundancy, the Stacked Denoising Autoencoder (SDAE) network is adopt to reduce the dimension of the feature vectors. Finally, we test the real network traffic and analyze the effect of model structure and external noise on the detection performance, and the experimental results verify the correctness of our scheme. Compared with other detection methods based on data reconstruction, the proposed method has higher detection accuracy and better detection performance.

Online Network traffic anomaly detection method combining OS-ELM and SADE