Adaptive Webpage Fingerprinting from TLS Traces
Vasilios Mavroudis, Jamie Hayes

TL;DR
This paper presents a scalable and accurate webpage fingerprinting method using TLS traffic analysis, highlighting its capabilities against modern adversaries and evaluating potential defenses in real-world scenarios.
Contribution
Introduces a TLS-specific webpage fingerprinting model that scales to many webpages, classifies unseen pages, and maintains low operational costs, advancing security analysis.
Findings
Scalable to thousands of webpages
Accurately classifies unseen pages
Low operational costs in dynamic environments
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Hate Speech and Cyberbullying Detection · Network Security and Intrusion Detection
