MFA-SGWNN:Botnet Detection Based on Multi-Feature Aggregation Spectral Graph Wavelet Neural Network
In botnet attacks,because the characteristics of disguised botnet traffic data are too similar to normal traffic data,it is difficult to distinguish them accurately by traditional detection methods.In order to solve this problem,this paper proposes a Multi-feature Aggregation Spectral Graph Wavelet Neural Network(MFA-SGWNN).This method combines the attribute and spatial features of traffic,which can effectively capture the hidden characteristics of infected host traffic,enhance the feature representation of botnet nodes,and avoid the influence of unbalanced data samples and malicious encrypted traffic on detection.Experimental results on the ISCX2014 botnet and CIC-IDS 2017(botnet)datasets show that MFA-SGWNN outperforms existing methods and has stronger robustness and generalization ability.
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