通信学报2025，Vol.46Issue(3)：221-233,13.DOI:10.11959/j.issn.1000-436x.2025043
基于对比学习和预训练Transformer的流量隐匿数据检测方法
Traffic concealed data detection method based on contrastive learning and pre-trained Transformer
摘要
Abstract
To solve the problems of characterizing representing massive encrypted traffic,perceiving malicious behav-iors,and identifying the ownership of privacy data,a traffic concealed data detection method was proposed based on con-trastive learning and pre-trained Transformer.Considering the high complexity,unstructured nature of encrypted traffic,and the insufficient performance of traditional fine-tuning methods for downstream tasks in the encrypted traffic domain,data packets were first transformed into tokens which was similar to those used in natural language processing.Then,a pre-trained Transformer model was utilized to convert shallow representations into a general traffic representation,which was suitable for various encrypted traffic downstream tasks.By transforming the problem of concealed data detection into a similarity analysis problem,a diversity-sensitive Transformer architecture was developed leveraging contrastive learning,which enhanced the model's sensitivity to traffic differences through the use of positive and negative sample pairs,and using information noise contrastive estimation(Info NCE)as the loss function for fine-tuning downstream tasks of encrypted traffic.Experimental results show that the proposed method outperforms mainstream methods in terms of accuracy,precision,recall and F1 score.关键词
流量隐匿数据检测/预训练Transformer模型/对比学习/加密流量Key words
traffic concealed data detection/pre-trained Transformer model/contrastive learning/encrypted traffic分类
计算机与自动化引用本文复制引用
何帅,张京超,徐笛,江帅,郭晓威,付才..基于对比学习和预训练Transformer的流量隐匿数据检测方法[J].通信学报,2025,46(3):221-233,13.基金项目
国家重点研发计划基金资助项目(No.2023YFB3106402) （No.2023YFB3106402）
国家自然科学基金资助项目(No.62072200) The National Key Research and Development Program of China(No.2023YFB3106402),The National Natural Science Foundation of China(No.62072200) （No.62072200）
