基于大语言模型的加密流量分析方法研究综述

沈蒙; 刘俣凯; 艾俊宇; 任琛琛; 沈泓烨; 李琦; 徐恪; 祝烈煌

doi:10.12263/DZXB.20251032

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基于大语言模型的加密流量分析方法研究综述

密态网络安全前沿技术 | 更新时间：2026-07-30

- 基于大语言模型的加密流量分析方法研究综述
- A Review of Encryption Traffic Analysis Methods Based on Large Language Models
- 电子学报 2026年54卷第4期页码：1425-1445
- 作者机构：
  
  1.北京理工大学网络空间安全学院，北京100081
  2.清华大学网络科学与网络空间研究院，北京100084
  3.清华大学计算机科学与技术系，北京100084
- 作者简介：
  
  沈蒙男，1988年1月出生于山东省德州市。现为北京理工大学网络空间安全学院教授、博士生导师。主要研究方向为网络加密流量分析、数据隐私安全、区块链应用。中国电子学会会员编号：E190019899M。E-mail: shenmeng@bit.edu.cn
  刘俣凯男，1998年6月出生于河北省石家庄市。北京理工大学网络空间安全学院博士研究生。主要研究方向为网络加密流量分析、恶意流量检测。中国电子学会会员编号：E190199178A。E-mail: liuyukai@bit.edu.cn
  艾俊宇男，2002年7月出生于河北省唐山市。北京理工大学网络空间安全学院硕士研究生。主要研究方向为网络加密流量分析、恶意流量检测。E-mail: zhaobufan@bit.edu.cn
  任琛琛女，2003年4月出生于河北省保定市。北京理工大学网络空间安全学院硕士研究生。主要研究方向为网络加密流量分析、网站指纹攻击。E-mail: chenchenren@bit.edu.cn
  沈泓烨女，2004年10月出生于江苏省苏州市。北京理工大学网络空间安全学院本科生。主要研究方向为网络加密流量分析、恶意流量检测。E-mail: 1120231080@bit.edu.cn
  李琦男，1979年1月出生于山东省东营市。现为清华大学网络研究院副教授、博士生导师。主要研究方向为互联网安全、人工智能安全。中国电子学会会员编号：E190026656S。E-mail: qli01@tsinghua.edu.cn
  徐恪男，1974年12月出生于江苏省淮安市。现为清华大学计算机系教授、博士生导师。主要研究方向为新一代互联网体系结构、网络空间安全。中国电子学会会员编号：E190008630F。E-mail: xuke@tsinghua.edu.cn
  祝烈煌男，1976年9月出生于浙江省衢州市。现为北京理工大学网络空间安全学院教授、博士生导师。主要研究方向为密码学、网络和信息安全。中国电子学会会员编号：E190010255M。E-mail: liehuangz@bit.edu.cn
- 基金信息：
  
  国家重点研发计划(2023YFB2703805);国家自然科学基金(U23A20304;U25A20428)
- DOI：10.12263/DZXB.20251032
  中图分类号： TP393.08;
- 收稿：2026-01-17，
  
  录用：2026-02-24，
  
  网络首发：2026-05-20，
  
  纸质出版：2026-04-25
- 稿件说明：
移动端阅览
沈蒙, 刘俣凯, 艾俊宇, 等. 基于大语言模型的加密流量分析方法研究综述[J]. 电子学报, 2026, 54(04): 1425-1445.

SHEN Meng, LIU Yukai, AI Junyu, et al. A Review of Encryption Traffic Analysis Methods Based on Large Language Models[J]. Acta Electronica Sinica, 2026, 54(04): 1425-1445.
沈蒙, 刘俣凯, 艾俊宇, 等. 基于大语言模型的加密流量分析方法研究综述[J]. 电子学报, 2026, 54(04): 1425-1445. DOI：10.12263/DZXB.20251032

SHEN Meng, LIU Yukai, AI Junyu, et al. A Review of Encryption Traffic Analysis Methods Based on Large Language Models[J]. Acta Electronica Sinica, 2026, 54(04): 1425-1445. DOI：10.12263/DZXB.20251032

摘要

随着网络安全技术的快速演进，主流应用、社交平台及内容服务已广泛采用加密协议来保护用户隐私。这一趋势使得加密流量逐步成为现代网络通信的主导形态，但也导致传统依赖明文载荷的深度包分析（Deep Packet Inspection，DPI）技术全面失效。在此背景下，加密流量分析作为保障网络基础设施安全、优化网络资源调度与维持服务质量的关键技术，已成为当前网络空间安全领域的研究重点。面对复杂多变的加密流量，传统机器学习方法高度依赖专家先验知识进行繁琐的手工特征设计，难以适应当前加密流量动态伪装的特点；而基于深度学习的分析方法虽在特征提取方面表现优异，但其高计算成本与大规模标注数据需求限制了在资源受限场景下的部署。近年来，大语言模型（Large Language Model，LLM）凭借其卓越的自监督预训练范式和强大的长序列上下文建模能力，为突破上述瓶颈提供了全新的技术路径。LLM将非结构化的流量字节或统计特征视为一种特殊的网络语言，实现了从专有小模型向通用基础模型底座的范式跃迁。本文聚焦流量分类、恶意流量检测、流量预测和流量生成四大核心下游任务，对基于大语言模型的加密流量分析方法进行了系统性总结和深入分析。首先，本文梳理了从流量采集、数据处理、特征提取、模型构建与微调到推理评估的通用技术流程。其次，针对四大下游任务，本文深入剖析了不同研究的技术演进脉络与架构创新：在流量分类方面，探讨了从基于BERT的判别式微调到基于生成式大模型零样本推理的演进，展现了模型在跨协议与少样本场景下的强大泛化适配能力；在恶意流量检测方面，详细论述了LLM作为特征编码器、分类器甚至可解释性决策器的多重角色，突出了其在应对多阶段隐蔽攻击与未知威胁时展现出的深层语义捕获优势；在流量预测方面，对比了传统时序模型与大模型自回归生成在捕获网络高维时空动态依赖上的差异；在流量生成方面，总结了模型如何跨越单纯的统计分布拟合，迈向受控、高保真且严格遵循协议状态机的流量生成。最后，本文总结了当前领域面临的数据分布偏移、部署资源受限等关键挑战，并从构建多模态数据生态、端云协同轻量化部署等维度展望了未来研究方向。

Abstract

With the rapid evolution of cybersecurity technologies

mainstream applications

social platforms

and content services have widely adopted encryption protocols to protect user privacy. This trend has made encrypted traffic the dominant form of modern network communication

but it has also rendered traditional deep packet inspection (DPI) techniques—which rely on plaintext payloads—completely ineffective. Against this backdrop

encrypted traffic analysis has emerged as a critical technology for safeguarding network infrastructure security

optimizing network resource allocation

and maintaining service quality

becoming a key research focus in the field of cyberspace security. Confronted with complex and dynamic encrypted traffic

traditional machine learning approaches heavily rely on expert-driven

cumbersome manual feature design

struggling to adapt to the dynamic obfuscation characteristics of modern encrypted traffic. While deep learning-based analysis methods excel in feature extraction

their high computational costs and requirements for large-scale labeled data limit deployment in resource-constrained scenarios. In recent years

large language models (LLMs) have emerged as a novel technical pathway to overcome these limitations

leveraging their exceptional self-supervised pre-training paradigms and robust long-sequence context modeling capabilities. LLMs treat unstructured traffic bytes or statistical features as a specialized network language

enabling a paradigm shift from specialized small models to universal foundational models. This paper systematically summarizes and deeply analyzes LLM-based cryptographic traffic analysis methods across four core downstream tasks: traffic classification

malicious traffic detection

traffic prediction

and traffic generation. First

it outlines the general technical workflow from traffic collection

data processing

feature extraction

model construction and fine-tuning

to inference evaluation. Second

for each downstream task

this paper delves into the technical evolution and architectural innovations across different studies: for traffic classification

it explores the progression from discriminative fine-tuning based on BERT to zero-shot reasoning using generative large models

demonstrating robust generalization capabilities across protocols and sparse data scenarios. For malicious traffic detection

we detail the multifaceted roles of LLMs as feature encoders

classifiers

and even explainable decision-makers

highlighting their superiority in capturing deep semantic patterns when confronting multi-stage covert attacks and unknown threats; For traffic prediction

we contrast traditional time-series models with large-model autoregressive generation in capturing high-dimensional spatiotemporal dependencies within networks. Regarding traffic generation

we summarize how models transcend mere statistical distribution fitting to achieve controlled

high-fidelity traffic generation that strictly adheres to protocol state machines. Finally

this paper summarizes key challenges facing the field

such as data distribution skew and constrained deployment resources

and outlines future research directions from dimensions including building a multimodal data ecosystem and enabling lightweight deployment through edge-cloud collaboration.

关键词

Keywords

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