摘要
对于加密流量的精确化识别,现有的基于机器学习和基于图的解决方案需要人工进行特征选择或者精度较低.使用一种基于图神经网络的加密流量识别方法,通过将网络流量数据转换为图数据,保留了网络数据流的丰富表示,将网络流量分类问题转换为图分类问题.并设计了一个基于自注意力机制的图分类模型进行加密流量的分类.实验结果表明,该方法对基于安全套接层(secure socket layer,SSL)的虚拟专用网(virtual private network,VPN)加密流量具有较好的分类效果,分类准确率有较大提高.
For precise identification of encrypted traffic,existing machine learning-based and graph-based solutions require manual feature selection or have low accuracy.Using a graph neural network-based encrypted traffic identification method,the network traffic classification problem is transformed into a graph classification problem by converting the network traffic data into graph data,preserving the rich representation of the network data flow.And this paper designs a graph classification model based on self-attention mechanism to classify encrypted traffic.The experimental results show that the method has a good classification effect on the encrypted traffic of Virtual Private Network(VPN)based on Secure Socket Layer(SSL),and the classification accuracy is greatly improved.
作者
喻晓伟
陈丹伟
Yu Xiaowei;Chen Danwei(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023)
出处
《信息安全研究》
CSCD
2023年第1期13-21,共9页
Journal of Information Security Research
关键词
加密流量分类
流量图
注意力机制
图神经网络
SSL加密流量
encrypted traffic classification
traffic graph
attention mechanism
graph neural network
SSL encrypted traffic
