摘要
针对传统加密流量分类方法准确率低、利用流量载荷会侵犯用户隐私,以及泛化能力弱的问题,提出一种基于注意力机制的CNN和BiGRU(CNN-AttBiGRU)的加密流量分类方法,可以同时适用于常规加密和VPN、Tor加密流量。该方法基于包大小、包到达时间以及包到达方向将流量转化为直观的图片,为提高模型准确率,使用CNN提取流量图片的空间特征,同时设计BiGRU和Self-attention模型提取时间特征,充分利用流量图片的时间和空间特征,可按照流量类别、加密技术和应用类型对流量进行不同层面的分类。该方法对加密流量类别分类的平均准确率达95.2%,较以往提升11.65%;对加密技术分类的准确率达95.5%,较以往提升7.1%;对流量所使用的应用程序分类的准确率达99.8%,较以往提升11.03%。实验结果表明,CNN-AttBiGRU方法的泛化能力强,并且其仅利用加密流量的部分统计特征,有效地保护了用户隐私,同时取得了高准确率。
To address the problems of low accuracy of traditional encrypted traffic classification methods,the use of traffic load will violate user privacy and weak generalization ability,an encrypted traffic classification method of CNN and BiGRU based on self-attention(CNN-AttBiGRU)is proposed,which can be applied to both regular encrypted and VPN and Tor encrypted traffic.The method converts traffic into intuitive pictures based on packet size,packet arrival time and packet arrival direction.To improve the accuracy of the model,CNN is used to extract the spatial features of traffic pictures,while BiGRU and self-attention models are designed to extract temporal features,making full use of the temporal and spatial features of traffic pictures.The traffic can be classified at different levels by traffic category,encryption technique and application type.The proposed method achieves an average accuracy of 95.2%for classification of encrypted traffic categories,which is 11.65%better than before;95.5%for classification of encryption technologies,which is 7.1%better than before;and 99.8%for classification of applications used by traffic,which is 11.03%better than before.Experimental results show that the CNN-AttBiGRU method has strong ge-neralization ability and only utilizes some statistical features of encrypted traffic,which effectively protects user privacy while achieving high accuracy rates.
作者
陈思雨
马海龙
张建辉
CHEN Siyu;MA Hailong;ZHANG Jianhui(School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450001,China;Institute of Information Technology,PLA Information Engineering University,Zhengzhou 450001,China;Songshan Laboratory,Zhengzhou 450001,China)
出处
《计算机科学》
CSCD
北大核心
2024年第8期396-402,共7页
Computer Science
基金
国家重点研发计划(2022YFB2901403)
河南省重大科技专项(221100210900-01)。
