摘要
在强对抗通信环境下,数据包传输易出现损坏或丢失的情况,会大幅降低网络流量分类的可靠性。传统的分类方法在一定程度上解决了流量分类问题,但难以准确刻画强对抗通信条件下流量的分类特征。针对上述问题,提出一种基于图神经网络的强对抗网络流量分类方法。采用随机提取完整流量中的数据构成流的方式模拟数据包损坏或丢失的传输情况。利用图结构表示流量,采用三种不同方式作为图的顶点特征,将构成的图传入图神经网络进行分类。最后在真实的网络流量数据集进行实验,实验结果表明,所提出的方法在分类精度上优于现有方法,具有更强的稳定性。
In strong confrontation communication environment,data packets are easy to be damaged or lost,which will greatly reduce the the reliability of network traffic classification.The traditional traffic classification method solves the problem of traffic classification to a certain extent,but it is difficult to accurately describe the classification characteristics of traffic under the condition of strong countermeasure communication.Aimed at the above problems,a strong adversarial network traffic classification method based on graph neural network is proposed.The data in the complete traffic was used to randomly extract the data to form a stream to simulate the transmission of data packet damage or loss.The graph structure was used to represent the flow,and three different methods were used as the vertex characteristics of the graph,and the constituent graph was passed into the graph neural network for classification.Experiments were carried out on the real network traffic dataset.The experimental results show that the proposed method is better than the existing methods in terms of classification accuracy and has stronger stability.
作者
蒋邦明
潘成胜
孔志翔
Jiang Bangming;Pan Chengsheng;Kong Zhixiang(School of Electronic&Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,Jiangsu,China;Institute of Intelligent Network and Information System,Nanjing University of Information Science and Technology,Nanjing 210044,Jiangsu,China;College of Automation,Nanjing University of Science and Technology,Nanjing 210094,Jiangsu,China)
出处
《计算机应用与软件》
北大核心
2025年第8期153-159,187,共8页
Computer Applications and Software
基金
国家自然科学基金重点项目(61931004)
国家自然科学基金项目(61801073)。
关键词
强对抗
流量分类
图神经网络
真实网络流量
Strong confrontation
Traffic classification
Graph neural network
Real network traffic
