北京大学学报（自然科学版）2024，Vol.60Issue(3)：403-412,10.DOI:10.13209/j.0479-8023.2024.035
MFA-SGWNN:基于多特征聚合谱图小波神经网络的僵尸网络检测
MFA-SGWNN:Botnet Detection Based on Multi-Feature Aggregation Spectral Graph Wavelet Neural Network
摘要
Abstract
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.关键词
僵尸网络/图小波神经网络/网络安全Key words
botnet/graph wavelet neural network/cyber security引用本文复制引用
吴悔,陈旭,景永俊,王叔洋..MFA-SGWNN:基于多特征聚合谱图小波神经网络的僵尸网络检测[J].北京大学学报（自然科学版）,2024,60(3):403-412,10.基金项目
北方民族大学中央高校基本科研业务费专项资金(2022PT_S04)和宁夏回族自治区重点研发项目(2023BDE02017)资助 （2022PT_S04）
