摘要
深度学习算法被广泛应用于网络流量分类领域并取得较好效果。然而对抗攻击的出现给其安全性带来了严重威胁,使得当前主流的基于卷积神经网络模型的分类算法的精度严重下降。针对此提出了一种抗流量分类中灰度图对抗攻击的加密流量分类方法。所提方法通过提取数据包负载长度、包序列、方向、簇等流量交互信息构建拓扑图,将加密流量分类问题转化为图分类问题。使用基于图卷积神经网络的分类方法进行特征的学习分类,图卷积神经网络模型可以自动从输入的拓扑图中提取特征,将特征映射到嵌入空间中的不同表示来区分不同的图结构。实验结果表明,所提方法不仅能够避免对抗攻击,且在公开数据集上的分类性能也较现有典型方法提高了5%以上。
Deep learning algorithms are widely used in the field of network traffic classification and have achieved good results. However, the emergence of adversarial attacks has brought a serious threat to its security, and the accuracy of the current mainstream classification algorithms based on convolutional neural network models has been seriously reduced. In response to this, this paper proposes an encrypted traffic classification method that resists gray-scale adversarial attacks in traffic classification. The proposed method constructs a topology graph by extracting traffic interaction information such as packet load length, sending order, direction, and cluster, and transforms the encrypted traffic classification problem into a graph classification problem. Then, this paper uses the classification method based on graph convolutional neural network to learn and classify features. The graph convolutional neural network model can automatically extract features from the input topology and map features to different representations in the embedding space to distinguish different graph structures. The experimental results show that the proposed method can not only avoid adversarial attacks, but also improve the classification performance on public datasets by more than 5% compared with the existing typical methods.
作者
王勤凡
翟江涛
陈伟
孙浩翔
Wang Qinfan;Zhai Jiangtao;Chen Wei;Sun Haoxiang(Collge of Eletronical and Information Enginering,Nanjing Urniversity of Information Science and Technology,Nanjing 210044,China)
出处
《电子测量技术》
北大核心
2022年第14期109-115,共7页
Electronic Measurement Technology
基金
国家自然科学基金(61931004,62072250)
南京信息工程大学人才启动基金(2020r061)项目资助。
关键词
网络流量分类
对抗攻击
图神经网络
深度学习
network traffic classification
adversarial attacks
graph neural network
deep learning
