# Adaptive Webpage Fingerprinting from TLS Traces

arXiv: https://arxiv.org/abs/2010.10294 · HTML: https://mlsanity.com/paper/2010.10294/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

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

- [Quantifying the Privacy Implications of High-Fidelity Synthetic Network Traffic](https://mlsanity.com/paper/2511.20497/) (2025)
- [Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems](https://mlsanity.com/paper/2411.09393/) (2024)

## Abstract (arXiv)

In webpage fingerprinting, an on-path adversary infers the specific webpage loaded by a victim user by analysing the patterns in the encrypted TLS traffic exchanged between the user's browser and the website's servers. This work studies modern webpage fingerprinting adversaries against the TLS protocol; aiming to shed light on their capabilities and inform potential defences. Despite the importance of this research area (the majority of global Internet users rely on standard web browsing with TLS) and the potential real-life impact, most past works have focused on attacks specific to anonymity networks (e.g., Tor). We introduce a TLS-specific model that: 1) scales to an unprecedented number of target webpages, 2) can accurately classify thousands of classes it never encountered during training, and 3) has low operational costs even in scenarios of frequent page updates. Based on these findings, we then discuss TLS-specific countermeasures and evaluate the effectiveness of the existing padding capabilities provided by TLS 1.3.