Fine-grained defense methods in federated encrypted traffic classification
In recent years,various robust algorithms and defense schemes have been presented to prevent the harm caused by abnormal traffic to the federal encrypted traffic classification model.The existing defense methods,which improve the robustness of the global model by removing the traffic of abnormal models,are coarse-grained.Nevertheless,the coarse-grained methods can lead to issues of excessive defense and normal traffic loss.To solve the above problems,we propose a fine-grained defense method to avoid abnormal traffic according to the collaborative federated encrypted traffic classification framework.The proposed method narrows the range of the abnormal traffic by dividing the local data set of abnormal nodes,achieving fine-grained localization of abnormal nodes.According to the localization results of abnormal traffic,the method realizes the fine-grained defense by eliminating abnormal traffic during model aggregation,which avoids the excessive defense and normal traffic loss.Experimental results show that the proposed method can significantly improve the efficiency of model detection without affecting accuracy.Compared with the existing coarse-grained methods,the accuracy of the fine-grained defense method can reach 91.4％,and the detection efficiency is improved by 32.3％.
万方数据
维普
