One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Peng Cheng Laboratory（鹏城实验室）；Southeast University（东南大学）；National University of Singapore（新加坡国立大学）
Haozhen Zhang, Xi Xiao, Le Yu, Qing Li, Zhen Ling, Ye Zhang
As network security receives widespread attention, encrypted traffic classification has become the current research focus. However, existing methods conduct traffic classification without sufficiently considering the common characteristics between data samples, leading to suboptimal performance. Moreover, they train the packet-level and flow-level classification tasks independently, which is redundant because the packet representations learned in the packet-level task can be exploited by the flow-level task. Therefore, in this paper, we propose an effective model named a Contrastive Learning Enhanced Temporal Fusion Encoder (CLE-TFE). In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning. We also propose cross-level multi-task learning, which simultaneously accomplishes the packet-level and flow-level classification tasks in the same model with one training. Further experiments show that CLE-TFE achieves the best overall performance on the two tasks, while its computational overhead (i.e., floating point operations, FLOPs) is only about 1/14 of the pre-trained model (e.g., ET-BERT). We release the code at https://github.com/ViktorAxelsen/CLE-TFE
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification
Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）；Peng Cheng Laboratory（鹏城实验室）；University of Molise（莫利塞大学）；IIT-CNR（意大利国家研究委员会信息与电信研究所）；Chinese Academy of Sciences（中国科学院）
MIETT: Multi-Instance Encrypted Traffic Transformer for Encrypted Traffic Classification
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）
Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Peng Cheng Laboratory（鹏城实验室）；Nanjing University of Posts and Telecommunications（南京邮电大学）；Southeast University（东南大学）；National University of Singapore（新加坡国立大学）
本文提出多视图异构图模型 MH-Net，通过多粒度流量单元、字节关联建模和多任务对比学习提升加密流量分类性能，并在 ISCX 与 CIC-IoT 上优于数十种 SOTA 方法。
Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis
School of Cyber Science and Engineering, Southeast University（东南大学网络安全科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）；Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University)（区块链应用、监督与管理工程研究中心（东南大学））；Engineering Research Center of Security for Ubiquitous Network, Jiangsu Province（江苏省物联网安全工程技术研究中心）
Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classification
School of Cyber Science and Engineering, Southeast University（东南大学网络科学与工程学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting
Zhejiang University（浙江大学）；South China University of Technology（华南理工大学）；The University of Sydney（悉尼大学）；Chongqing University（重庆大学）；Zhejiang Institute of Industry and Information Technology（浙江工业信息技术研究院）
College of Artificial Intelligence and Automation, Hohai University, China（人工智能与自动化学院，河海大学，中国）；Zhongguancun Laboratory, Beijing, China（中关村实验室，北京，中国）；College of Information Science and Engineering, Hohai University, China（信息科学与工程学院，河海大学，中国）
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning
Fujian Power Co., Ltd. Electric Power Research Institute（福建电力有限公司电力科学研究院）；State Grid Fujian Electric Power Co., Ltd.（国网福建省电力有限公司）；Institute of Cyberspace Security, Zhejiang University of Technology（浙江工业大学网络空间安全研究院）；Binjiang Cyberspace Security Institute of ZJUT（浙江工业大学滨江网络空间安全研究院）
Distinguishing Tor From Other Encrypted Network Traffic Through Character Analysis
King Mongkut’s University of Technology North Bangkok（北曼谷国王科技大学）；Fraunhofer Institute for Applied and Integrated Security（弗劳恩霍夫应用与集成安全研究所）；Ostbayerische Technische Hochschule Amberg-Weiden（东巴伐利亚应用科学大学安贝格-魏登分校）
From Packets to Patterns: Interpreting Encrypted Network Traffic as Longitudinal Behavioral Signals
New York University（纽约大学）；NYU Langone Health（NYU Langone健康）；NYU Grossman School of Medicine（NYU Grossman医学院）；Oregon Health & Science University（俄勒冈健康与科学大学）；Columbia University（哥伦比亚大学）；Harvard Medical School（哈佛医学院）
Encrypted Internet traffic classification using a supervised Spiking Neural Network
Institut Supérieur de l’Aéronautique et de l’Espace (ISAE-SUPAERO)（高等航空航天学院）；University of Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）；Université Toulouse 3（图卢兹第三大学）；Sharif University of Technology（谢里夫理工大学）
Listen to Minority: Encrypted Traffic Classification for Class Imbalance with Contrastive Pre-Training
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Amazon（亚马逊）；Zhongguancun Laboratory（中关村实验室）；China Assets Cybersecurity Technology CO.,Ltd.（中资网络安全科技有限公司）
When a RF Beats a CNN and GRU, Together -- A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification
Tracking without Seeing: Geospatial Inference using Encrypted Traffic from Distributed Nodes
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
PACC: Protocol-Aware Cross-Layer Compression for Compact Network Traffic Representation
University of Electronic Science and Technology of China（电子科技大学）；University of Hong Kong（香港大学）；Renmin University of China（中国人民大学）；Hong Kong Polytechnic University（香港理工大学）
