# Towards Robust Multi-tab Website Fingerprinting

arXiv: https://arxiv.org/abs/2501.12622 · HTML: https://mlsanity.com/paper/2501.12622/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2025.

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

- [GETA: Generalized Encrypted Traffic Analysis](https://mlsanity.com/paper/2605.31277/) (2026)
- [ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing](https://mlsanity.com/paper/2610.06514/) (2026)

## Abstract (arXiv)

Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using the novel Transformer-based models. Specifically, ARES extracts local patterns based on multi-level traffic aggregation features and utilizes the improved self-attention mechanism to analyze the correlations between these local patterns, effectively identifying websites. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale datasets collected over multiple months. The experimental results illustrate that ARES achieves optimal performance in several realistic scenarios. Further, ARES remains robust even against various WF defenses.