.png){kind=icon}
.png){kind=icon}
Return
Toward Robust Multi-Tab Website Fingerprinting
.png){kind=icon}
PRE
AI
DOI：10.1109/TON.2026.3666721.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 $\textsf {ARES}$ , a novel WF framework natively designed for multi-tab WF attacks. $\textsf {ARES}$ formulates the multi-tab attack as a multi-label classification problem and solves it using the novel Transformer-based models. Specifically, $\textsf {ARES}$ 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 $\textsf {ARES}$ and extensively evaluate its effectiveness using our large-scale datasets collected over multiple months. The experimental results illustrate that $\textsf {ARES}$ achieves optimal performance in several realistic scenarios. Further, $\textsf {ARES}$ remains robust even against various WF defenses.
Keywords:
Website fingerprinting attack
deep learning
traffic analysis
Journal
I
IF:
0
Papers:
543
Citations:
0