Encrypted Traffic Identification by Fusing Softmax Classifier with Its Angular Margin Variant
Yan Lin, Mingyong ZENG, Shuai REN, Zhangkai Luo · IEICE Transactions on Information and Systems · 2021
Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.