About

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.

Datasets

• 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)
• MobileTraffic(300+ Mobile Apps)
• Itc-Net-Blend-60(Android applications in Diverse Environments)

Survey

• 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.

Network Traffic Classification

Traditional Target (Web, App, Malware, Gambling)

• Gambling Domain Name Recognition via Certificate and Textual Analysis. GuoYing Sun. The Computer Journal 2023.
• Analyzing Ground-Truth Data of Mobile Gambling Scams. Geng Hong. Symposium on Security and Privacy(S&P) 2022.
• 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.