基于RBF神经网络的可信加密流量分类方法

网络流量分类广泛应用于网络资源分配、流量调度、入侵检测系统等研究领域。随着加密协议的普及和网络流量快速发展，基于深度学习的流量分类器由于其自动提取特征的特性和较高的分类准确性，逐渐受到科研人员的重视，但是面向网络流量分类的可信程度方面却不曾有研究。本文提出一种基于RBF神经网络对加密网络流量进行可信分类的方法。所提算法建立在RBF网络的思想上并采用一种新的损失函数和质心更新方案来进行训练，通过使用梯度惩罚强制检测输入的变化，能够有效地检测分布外的数据。在2个公共的ISCX VPN-nonVPN和USTC-TFC2016流量数据集上，与同类算法相比，所提算法取得了最好的分布外检测结果，在AUROC指标上达到98.55%。实验结果表明所提算法在具有较高分类性能的同时，能够有效地检测出分布外的流量数据，从而提高流量分类的可信性。

Trustworthy Encryption Traffic Classification Method Based on RBF Neural Network

Network traffic classification is widely used in research fields such as network resource allocation,traffic scheduling and intrusion detection systems.With the popularization of encryption protocols and the rapid development of network traffic,the traffic classifier based on deep learning has gradually attracted the attention of researchers due to its feature of automatically extracting features and high classification accuracy.But there has been no research on the credibility of network traffic classification.This article proposes a trustworthy deep learning model to classify encrypted network traffic.The proposed algorithm is based on the idea of RBF network and uses a new loss function and centroid update scheme for training.By using gradient penalty to force detection of input changes,it can effectively detect out-of-distribution data.On the two public ISCX VPN-nonVPN and USTC-TFC2016 traffic data sets,the proposed algorithm achieves 98.55%of the AUROC index,and has achieved the best out-of-distribution detection effect compared with similar algorithms.Extensive experimental results show that the proposed algorithm has high classification performance and can effectively detect out-of-distribution traffic data,which improves the credibility of the traffic classification model.