Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (6): 161-173.doi: 10.3969/j.issn.1000-5641.2021.06.016

• Computer Science • Previous Articles    

Enabling self-attention based multi-feature anomaly detection and classification of network traffic

Yuting HUANGFU, Liying LI, Haizhou WANG, Fuke SHEN, Tongquan WEI*()   

  1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
  • Received:2020-10-21 Online:2021-11-25 Published:2021-11-26
  • Contact: Tongquan WEI


Network traffic anomaly detection based on feature selection has attracted great research interest. Most existing schemes detect anomalies by reducing the dimensionality of traffic data, but ignore the correlation between data features; this results in inefficient detection of anomaly traffic. In order to effectively identify various types of attacks, a model based on a self-attentive mechanism is proposed to learn the correlation between multiple features of network traffic data. Then, a novel multi-feature anomalous traffic detection and classification model is designed, which analyzes the correlation between multiple features of the anomalous traffic data and subsequently identifies anomalous network traffic. Experimental results show that, compared to two benchmark methods, the proposed technique increased the accuracy of anomaly detection and classification by a maximum of 1.65% and reduced the false alarm rate by 1.1%.

Key words: network anomaly detection, network anomaly classification, self-attention, feature selection, multi-feature correlation

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