基于多维特征学习的加密移动应用流量分类方法  

Classification Method for Encrypted Mobile Application Traffic Based on Multi-dimensional Feature Learning

作　　者：孙泽沛 陈江涛 王子豪 潘炜[1] SUN Zepei;CHEN Jiangtao;WANG Zihao;PAN Wei(School of Computer Science,Northwestern Polytechnical University,Xi an 710072,China)

机构地区：[1]西北工业大学计算机学院,西安710072

出　　处：《网络空间安全科学学报》2023年第3期13-24,共12页Journal of Cybersecurity

基　　金：国家自然科学基金联合基金项目(U22B2025)。

摘　　要：加密移动应用网络流量的准确分析与有效识别,是网络管理、信息监管、安全检测的重要前置条件,对网络空间安全与治理具有重要意义。为了对加密移动应用流量进行有效识别,提出了一个基于多维特征学习的加密移动应用流量分类方法。该方法首先从移动应用流量中提取传输层负载特征和会话交互特征,然后建立多维特征深度学习模型,利用卷积神经网络学习负载数据中的空间特征、长短时记忆网络学习负载数据中的时序特征以及利用图卷积神经网络学习移动应用的会话交互特征,进一步实现多维特征的拼接融合,达到加密移动应用流量的分类识别目的。基于加密移动应用数据集,实验验证了所提出的方法在加密移动应用流量分类方面的性能优化效果。The accurate analysis and identification of encrypted mobile application traffic can provide an important technical support for network management,information supervision,and security detection,etc.It is of great significance to cyberspace security and governance.A classification method based on multi-dimensional feature learning was proposed to effectively identify encrypted mobile application traffic.Firstly,this method extracted the transport layer payload and session features from the mobile application traffic,then built the multi-dimensional feature deep learning model.The convolutional neural network was used to learn the spatial features of payloads,the long short-term memory network was used to learn the time series features of encrypted flows,and the graph convolutional neural network was used to learn the session features of the mobile application,and further concatenate and fuse the multi-dimensional features,achieving the classification and identification of encrypted mobile application traffic.Based on the encrypted mobile application dataset,the experimental results show compared to other classification models,the proposed method has an optimized performance in encrypted traffic classification for mobile applications.

关 键 词：移动应用 加密流量分类 多维特征学习 图卷积网络 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]