TSA-WF: Exploring the Effectiveness of Time Series Analysis for Website Fingerprinting
Michael Wrana, Uzma Maroof, Diogo Barradas

TL;DR
This paper investigates the use of classical time series analysis techniques for website fingerprinting, demonstrating comparable accuracy in single-tab scenarios and potential for pinpointing website visit times in multi-tab traces.
Contribution
Introduces TSA-WF, a novel pipeline that leverages time series analysis to improve website fingerprinting, especially for timing estimation within network traces.
Findings
TSA-WF achieves accuracy comparable to existing attacks in single-tab scenarios.
TSA-WF can identify the approximate timing of website visits in multi-tab traces.
The approach remains effective even against certain WF defenses.
Abstract
Website fingerprinting (WF) is a technique that allows an eavesdropper to determine the website a target user is accessing by inspecting the metadata associated with the packets she exchanges via some encrypted tunnel, e.g., Tor. Recent WF attacks built using machine learning (and deep learning) process and summarize trace metadata during their feature extraction phases. This methodology leads to predictions that lack information about the instant at which a given website is detected within a (potentially large) network trace comprised of multiple sequential website accesses -- a setting known as \textit{multi-tab} WF. In this paper, we explore whether classical time series analysis techniques can be effective in the WF setting. Specifically, we introduce TSA-WF, a pipeline designed to closely preserve network traces' timing and direction characteristics, which enables the exploration…
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 · Network Security and Intrusion Detection · Advanced Steganography and Watermarking Techniques
