Unknown-Traffic Detection, Calibration and Shortcut Reliance in Distilled Encrypted-Traffic Classifiers over One Year
cs.NI, cs.LG
Submitted: 2026-09-25
Updated: 2026-09-25
Code: https://github.com/Mahmoud-Abbasi-svg/kd-encrypted-traffic-inheritance
Terminology
Sources
- netFound: Principled Design for Network Foundation Models
- Distilling the Knowledge in a Neural Network
- Distillation-Enhanced Clustering Acceleration for Encrypted Traffic Classification
- MERLOT: A Distilled LLM-based Mixture-of-Experts Framework for Scalable Encrypted Traffic Classification
- ResAware: Cross-Environment Website Fingerprinting via Resource-Privileged Distillation
- When Simple Model Just Works: Is Network Traffic Classification in Crisis?
- Fine-grained TLS services classification with reject option
- Bias in the Shadows: Explore Shortcuts in Encrypted Network Traffic Classification
- A Functional Perspective on Knowledge Distillation in Neural Networks
- Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification
- Does Knowledge Distillation Really Work?
- What Knowledge Gets Distilled in Knowledge Distillation?
- Beyond Dark Knowledge: Mixup-Based Distillation for Reliable Predictions
- Trust the uncertain teacher: distilling dark knowledge via calibrated uncertainty
- The Role of Teacher Calibration in Knowledge Distillation
- Knowledge Distillation Must Account for What It Loses
- Who Taught You That? Tracing Teachers in Model Distillation
- Knowledge Distillation Detection for Open-weights Models
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