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This is a current list of resources related to the research and development of encrypted traffic analysis. We comb the field for relevant representative work and related resources, and pay more attention to typical studies and research teams.
- Canadian Institute for Cybersecurity Datasets (DNS, IDS, DoS, Darknet, Tor, VPN, Botnet, Malware)
- Cross-Platform (iOS and Android Apps)
- Malware Capture Facility Project (Malware)
- CSTNET-TLS 1.3 (TLS 1.3 services)
- Network-based Intrusion Detection (AWID, Botnet, CIC DoS, CICIDS, CIDDS, CTU, DARPA, ISCX, IRSC)
- Deep Learning for Encrypted Traffic Classification: An Overview. Shahbaz Rezaei. IEEE Communications Magazine 2019.
- Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey. Fannia Pacheco. IEEE Communications Surveys & Tutorials 2019.
- Deep Learning in Mobile and Wireless Networking: A Survey. Chaoyun Zhang. IEEE Communications Surveys & Tutorials 2019.
- Classifying encrypted traffic using adaptive fingerprints with multi-level attributes. Chang Liu. WWW Journal 2021.
- CETAnalytics: Comprehensive effective traffic information analytics for encrypted traffic classification (Generalization). Cong Dong. Computer Networks 2020.
- FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic. van Ede, Thijs. NDSS 2020. [code]
- FS-Net: A Flow Sequence Network For Encrypted Traffic Classification. Chang Liu. INFOCOM 2019. [code]
- MaMPF: Encrypted Traffic Classification Based on Multi-Attribute Markov Probability Fingerprints. Chang Liu. IWQoS 2018. [code]
- AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic. Vincent F. Taylor. EuroS&P 2016. [code]
- Online Website Fingerprinting: Evaluating Website Fingerprinting Attacks on Tor in the Real World. Giovanni Cherubin. Usenix Security 2022.
- BAPM: Block Attention Profiling Model for Multi-tab Website Fingerprinting Attacks on Tor. Zhong Guan. ACSAC 2021.
- Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning. Payap Sirinam. CCS 2018. [code]
- Walls Have Ears: Traffic-based Side-channel Attack in Video Streaming. Jiaxi Gu. IEEE INFOCOM 2018.
- ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification. Xinjie Lin. WWW 2022. [code]
- Accurate mobile-app fingerprinting using flow-level relationship with graph neural networks. Minghao Jiang. Computer Networks 2022.
- Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning. Payap Sirinam. CCS 2019. [code]
- 6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement Learning. Tianyu Cui. INFOCOM 2021. [code]
- SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network. Tianyu Cui. USENIX 2021. [code]
- 6VecLM: Language Modeling in Vector Space for IPv6 Target Generation. Tianyu Cui. ECML/PKDD 2020. [code]
- Tao Wang (Simon Fraser University/Canada)
- Haixin Duan (Tsinghua University/China)
- Xiaofeng Wang (Indiana University Bloomington/United States)
Version 1.0
April 15, 2022
- Welcome to the Ph.Ds from IIE,CAS.
Thanks goes to these wonderful people!
 Xinjie Lin 🎯 📝 📔 |
 Tianyu Cui 🎯 |
 Minghao Jiang 🎯 |
 Zhong Guan 🎯 📝 |
 Wei Cai 🎯 |