加密隧道审查规避技术及流量分析研究综述

吕梦达; 胡晓艳; 卢俊羲; 李清昀; 吴桦; 袁亚丽; 张帆

doi:10.12263/DZXB.20251052

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加密隧道审查规避技术及流量分析研究综述

密态网络安全前沿技术 | 更新时间：2026-07-30

- 加密隧道审查规避技术及流量分析研究综述
- A Review of Techniques for Bypassing Censorship in Encrypted Tunnels and Traffic Analysis
- 电子学报 2026年54卷第4期页码：1393-1424
- 作者机构：
  
  1.东南大学网络空间安全学院，江苏南京 211189
  2.网络通信与安全紫金山实验室，江苏南京 211189
  3.区块链应用监管教育部工程研究中心（东南大学），江苏南京 211189
  4.浙江大学计算机科学与技术学院，浙江杭州 310027
- 作者简介：
  
  吕梦达男，1998年12月出生于山东省菏泽市。现为东南大学网络空间安全学院博士研究生。主要研究方向为加密流量分析。中国电子学会会员编号：E190202388A。E-mail: mdlyu@seu.edu.cn
  胡晓艳女，1985年7月出生于江西省抚州市。2012年博士毕业于东南大学，现为东南大学网络空间安全学院副教授。主要研究方向为加密流量分析、网络空间安全、未来网络体系结构。 E-mail: xyhu@seu.edu.cn
  卢俊羲男，1998年3月出生于山东省淄博市。现为东南大学网络空间安全学院博士研究生。主要研究方向为钓鱼网站检测与域名安全。E-mail: jxlu@seu.edu.cn
  李清昀男，2000年2月出生于陕西省宝鸡市。现为东南大学网络空间安全学院硕士研究生。主要研究方向为加密流量分析。 E-mail: 220235248@seu.edu.cn
  吴桦女，1973年4月出生于江苏省南京市。2010年博士毕业于东南大学，现为东南大学网络空间安全学院副教授。主要研究方向为加密流量分析、网络空间安全、网络态势感知。E-mail: hwu@seu.edu.cn
  袁亚丽女，1986年2月出生于河南省周口市。2018年毕业于德国哥廷根大学，现为东南大学网络空间安全学院副教授。主要研究方向为网络流量安全分析、协议逆向、漏洞挖掘、网络攻防、暗网、人工智能安全。中国电子学会会员编号：E190202061M。 E-mail: yaliyuan@seu.edu.cn
  张帆男，1978年10月出生于浙江省杭州市。2011年博士毕业于美国康涅狄格大学，现为浙江大学计算机科学与技术学院教授。主要研究方向为硬件安全、物联网安全、网络安全、系统安全、密码学等。 E-mail: fanzhang@zju.edu.cn
- 基金信息：
  
  国家重点研发计划(2023YFB3106801);国家自然科学基金(62472087);江苏省自然科学基金(BK20231413);中央高校基本科研业务费(2242025K30025)
- DOI：10.12263/DZXB.20251052
  中图分类号： TP393;
- 收稿：2026-01-12，
  
  录用：2026-02-03，
  
  网络首发：2026-05-28，
  
  纸质出版：2026-04-25
- 稿件说明：
移动端阅览
吕梦达, 胡晓艳, 卢俊羲, 等. 加密隧道审查规避技术及流量分析研究综述[J]. 电子学报, 2026, 54(04): 1393-1424.

LÜ Mengda, HU Xiaoyan, LU Junxi, et al. A Review of Techniques for Bypassing Censorship in Encrypted Tunnels and Traffic Analysis[J]. Acta Electronica Sinica, 2026, 54(04): 1393-1424.
吕梦达, 胡晓艳, 卢俊羲, 等. 加密隧道审查规避技术及流量分析研究综述[J]. 电子学报, 2026, 54(04): 1393-1424. DOI：10.12263/DZXB.20251052

LÜ Mengda, HU Xiaoyan, LU Junxi, et al. A Review of Techniques for Bypassing Censorship in Encrypted Tunnels and Traffic Analysis[J]. Acta Electronica Sinica, 2026, 54(04): 1393-1424. DOI：10.12263/DZXB.20251052

摘要

网络通信的加密化已成为不可逆转的趋势，在提升用户隐私安全基线的同时，也为恶意活动与审查规避提供了可乘之机。加密隧道技术因其独特的双重用途特性，成为网络攻防博弈的焦点。本文将虚拟专用网络（Virtual Private Network， VPN）和加密代理技术作为加密隧道的代表性技术，系统阐释其核心通信机理、典型协议形态与主流规避策略，并构建了涵盖被动流量监听与主动探针探测能力的现实威胁模型，明确了审查方在连接级识别、行为级分类及端到端关联等维度的能力边界。在此基础上，本文从数据集构建、特征提取方法、分析算法和研究动态四个维度，分别对VPN和加密代理的流量分析研究现状进行了深入梳理与对比。首先，本文分析了数据集构建现状，将其分为公开数据集与自采数据集，指出公开数据集存在时效性滞后与协议覆盖不足的问题，而自采数据集虽具有针对性，但其面临采集环境单一与复现困难的挑战。其次，本文从包级别、流级别、主机级别以及深度学习维度展开介绍了特征提取方法，系统归纳了从微观序列指纹到宏观行为图谱的多层次特征工程技术。然后，本文梳理了分析算法的演进路径，展示了从依赖专家知识的规则匹配，到基于统计规律的机器学习，再到具备强大端到端表征能力的深度学习算法的发展脉络，并总结了流量检测、业务分类、指纹识别及生态审计等主要研究动态。最后，本文提炼了当前研究在面对协议伪装等高级规避技术时所面临的核心挑战，包括规避协议快速迭代引发的概念漂移、多路复用与快速UDP互联网连接（Quick UDP Internet Connections，QUIC）协议连接迁移导致的流定义失效、实验室环境与现网威胁模型的差异、深度学习模型的可解释性缺失以及大规模背景流量下的基本比率谬误，并据此展望了协议无关检测、在线部署模型设计、自动化基准数据集构建及生成式隐写技术探索等未来研究方向。

Abstract

The encryption of network traffic has become an irreversible trend. While enhancing the baseline for user privacy and security

it also creates opportunities for malicious activities and censorship evasion. Encrypted tunneling technology

owing to its unique dual-use nature

has emerged as a focal point in the adversarial network landscape. This paper regards virtual private networks (VPNs) and encrypted proxies as representative encrypted tunneling technologies. It systematically elucidates their core communication mechanisms

typical protocol morphologies

and mainstream evasion strategies. Furthermore

a realistic threat model encompassing passive traffic monitoring and active probing capabilities is constructed to delineate the censor’s capability boundaries in connection-level identification

behavioral classification

and end-to-end correlation. Building upon this foundation

this paper presents an in-depth review and comparative analysis of the current research landscape regarding traffic analysis for VPNs and encrypted proxies. This analysis is structured across four dimensions: dataset construction

feature extraction methodologies

analytical algorithms

and research dynamics. First

the status of dataset construction is analyzed by categorizing datasets into public and self-collected types. The paper points out that public datasets suffer from issues such as timeliness lag and insufficient protocol coverage

whereas self-collected datasets

despite their targeted nature

face challenges related to singular collection environments and difficulties in reproducibility. Second

feature extraction methodologies are detailed across packet-level

flow-level

and host-level dimensions

and

in the deep learning domain

systematically summarized

ranging from microscopic sequence fingerprints to macroscopic behavioral graphs. Third

the evolutionary path of analytical algorithms is outlined

demonstrating the development trajectory from rule-based matching reliant on expert knowledge

to machine learning based on statistical laws

and finally to deep learning algorithms possessing powerful end-to-end representation capabilities. Additionally

major research dynamics

including traffic detection

service classification

fingerprint identification

and ecosystem auditing

are summarized. Finally

this paper distills the core challenges currently faced in confronting advanced evasion techniques such as protocol imitation. These challenges include concept drift triggered by rapid iteration of evasion protocols

flow-definition failures caused by multiplexing and quick UDP Internet connections (QUIC) connection migration

discrepancies between laboratory environments and real-world threat models

the lack of interpretability in deep learning models

and the base-rate fallacy under large-scale background traffic. Based on these challenges

future research directions include protocol-agnostic detection

the design of online deployment models

automated construction of benchmark datasets

and the exploration of generative steganography techniques.

关键词

Keywords

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