Encrypted Traffic Classification
AI-powered classification of encrypted network traffic — enabling security monitoring and QoS management without compromising encryption or user privacy.
Over 90% of internet traffic is now encrypted. While encryption protects privacy, it makes traditional network monitoring impossible. We build AI systems that classify and analyze encrypted traffic without decryption — enabling security, quality of service, and compliance while preserving user privacy.
Key Contributions
- PQClass: Post-Quantum Traffic Classification — The first system to classify traffic encrypted with post-quantum algorithms (CRYSTALS-Kyber, NTRU), ensuring network visibility during the quantum cryptography transition.
- GAN-Based Data Synthesis — Generative adversarial networks that produce realistic encrypted traffic datasets, addressing the scarcity of shareable labeled data in this domain.
- Spectral Analysis for Protocol Identification — Frequency-domain analysis of packet inter-arrival times and sizes reveals application fingerprints inside TLS 1.3 and ESNI without any payload inspection.
- Zero-Day Attack Detection — SimCSE-based contrastive learning builds traffic representations that generalize to unseen attack patterns without retraining on labeled attack data.
Impact
Our methods are validated on real-world datasets from commercial ISPs and academic network captures. Applications include real-time threat detection in enterprise networks, QoE optimization for streaming services, and network capacity planning — all with full encryption preserved.
Related Publications
2026
- Quality of Experience Prediction for First Person Shooter Online Gaming: The Case Study of Call of Duty2026Proceedings of the IEEE Consumer Communications & Networking Conference (CCNC 2026)
2025
- Enhancing Encrypted Internet Traffic Classification Through Advanced Data Augmentation Techniques2025Proceedings of the IEEE International Conference on Communications
- PQClass: Classification of Post-Quantum Encryption Applications in Internet Traffic2025Proceedings of the IEEE International Conference on Communications
- A Classification-by-Retrieval Framework for Few-Shot Anomaly Detection to Detect API Injection2025Computers & Security
2024
- The Art of Time-Bending: Data Augmentation and Early Prediction for Efficient Traffic Classification2024Expert Systems with Applications
- CBR–Boosting Adaptive Classification By Retrieval of Encrypted Network Traffic with Out-of-Distribution2024arXiv preprint arXiv:2403.11206
- OSF-EIMTC: An Open-Source Framework for Standardized Encrypted Internet Traffic Classification2024Computer Communications
- Hidden in Time, Revealed in Frequency: Spectral Features and Multiresolution Analysis for Encrypted Internet Traffic Classification20242024 IEEE 21st Consumer Communications & Networking Conference (CCNC)
- Revolutionizing Our Way to Better Classifiers: Leveraging Synthetic Data with Generative Models for Encrypted Network Traffic Classification2024Available at SSRN 4654236
2023
- When a RF Beats a CNN and GRU, Together—A Comparison of Deep Learning and Classical Machine Learning Approaches for Encrypted Malware Traffic Classification2023Computers & Security
2022
- SimCSE for Encrypted Traffic Detection and Zero-Day Attack Detection2022IEEE Access
- MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification20222022 IEEE 19th annual consumer communications & networking conference (CCNC)
2021
- PCL: Packet Classification with Limited Knowledge2021IEEE INFOCOM 2021-IEEE Conference on Computer Communications
2020
- Encrypted Video Traffic Clustering Demystified2020Computers & Security
- Robust Machine Learning for Encrypted Traffic Classification2020CoRR