# ResAware: Cross-Environment Website Fingerprinting via Resource-Privileged Distillation

arXiv: https://arxiv.org/abs/2606.17462 · HTML: https://mlsanity.com/paper/2606.17462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Revisiting the Calibration of Modern Neural Networks](https://mlsanity.com/paper/2106.07998/) (2021)
- [Unifying distillation and privileged information](https://mlsanity.com/paper/1511.03643/) (2015)
- [Knowledge Distillation from A Stronger Teacher](https://mlsanity.com/paper/2205.10536/) (2022)
- [Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation](https://mlsanity.com/paper/1806.01156/) (2018)
- [Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning](https://mlsanity.com/paper/1801.02265/) (2018)
- [k-fingerprinting: a Robust Scalable Website Fingerprinting Technique](https://mlsanity.com/paper/1509.00789/) (2015)
- [Automated Website Fingerprinting through Deep Learning](https://mlsanity.com/paper/1708.06376/) (2017)
- [Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning](https://mlsanity.com/paper/1802.10215/) (2018)
- [Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks](https://mlsanity.com/paper/1902.06421/) (2019)
- [Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic](https://mlsanity.com/paper/2302.13763/) (2023)
- [Few-Shot Website Fingerprinting Attack](https://mlsanity.com/paper/2101.10063/) (2021)

## Built on by (most cited first, 2 of 2)

- [CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts](https://mlsanity.com/paper/2608.13905/) (2026)
- [Unknown-Traffic Detection, Calibration and Shortcut Reliance in Distilled Encrypted-Traffic Classifiers over One Year](https://mlsanity.com/paper/2609.31141/) (2026)

## Abstract (arXiv)

While Website Fingerprinting (WF) attacks achieve high accuracy in controlled laboratory settings, they often degrade substantially in real-world environments due to spatio-temporal drift, browser heterogeneity, proxy obfuscation and etc. This limitation stems from their sole reliance on low-level traffic features that are noisy and highly sensitive to environmental perturbations. To address this problem, we propose ResAware, a cross-environment resource-aware distillation framework under a textittraining-rich/inference-poor asymmetric setting. Specifically, ResAware trains a teacher model on resource-level features, and then distills the resulting privileged knowledge into a student model through heterogeneous knowledge distillation. At deployment time, the student model performs inference using only encrypted traffic, incurring zero additional cost. We evaluate ResAware on a large-scale dataset collected over five months from six globally distributed vantage points, comprising more than 160,000 paired samples. The results show that ResAware significantly enhances the cross-environment robustness of diverse WF baselines. Under a 150-day temporal drift, for example, ResAware improves the F1-score of Var-CNN from 72.77% to 81.49% and the open-world TPR@1%FPR from 22.40% to 27.20%. Our results demonstrate that resource-level supervision improves WF robustness without expanding online observation capabilities.