# Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis

arXiv: https://arxiv.org/abs/2407.00918 · HTML: https://mlsanity.com/paper/2407.00918/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning](https://mlsanity.com/paper/1801.02265/) (2018)
- [k-fingerprinting: a Robust Scalable Website Fingerprinting Technique](https://mlsanity.com/paper/1509.00789/) (2015)
- [Automated Website Fingerprinting through Deep Learning](https://mlsanity.com/paper/1708.06376/) (2017)
- [Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning](https://mlsanity.com/paper/1802.10215/) (2018)
- [Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks](https://mlsanity.com/paper/1902.06421/) (2019)
- [Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic](https://mlsanity.com/paper/2309.04798/) (2023)
- [Realistic Website Fingerprinting By Augmenting Network Trace](https://mlsanity.com/paper/2309.10147/) (2023)
- [Toward an Efficient Website Fingerprinting Defense](https://mlsanity.com/paper/1512.00524/) (2015)
- [RegulaTor: A Straightforward Website Fingerprinting Defense](https://mlsanity.com/paper/2012.06609/) (2020)
- [Understanding Tor Usage with Privacy-Preserving Measurement](https://mlsanity.com/paper/1809.08481/) (2018)
- [Website fingerprinting on early QUIC traffic](https://mlsanity.com/paper/2101.11871/) (2021)

## Built on by (most cited first, 8 of 8)

- [Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions](https://mlsanity.com/paper/2510.07176/) (2025)
- [STAR: Semantic-Traffic Alignment and Retrieval for Zero-Shot HTTPS Website Fingerprinting](https://mlsanity.com/paper/2512.17667/) (2025)
- [IntraGuard: Committee-Side Defenses Against Review Outsourcing to Commercial Chatbots](https://mlsanity.com/paper/2605.05271/) (2026)
- [Versatile yet Efficient Network Traffic Analysis: Offloading Network Foundation Model to SmartNIC](https://mlsanity.com/paper/2508.02001/) (2025)
- [Beyond a Single Perspective: Towards a Realistic Evaluation of Website Fingerprinting Attacks](https://mlsanity.com/paper/2510.14283/) (2025)
- [A Modular Framework for Rapidly Building Intrusion Predictors](https://mlsanity.com/paper/2511.23000/) (2025)
- [MalMoE: Mixture-of-Experts Enhanced Encrypted Malicious Traffic Detection Under Graph Drift](https://mlsanity.com/paper/2602.10157/) (2026)
- [Conformal Prediction for Offensive Security](https://mlsanity.com/paper/2609.05165/) (2026)

## Abstract (arXiv)

Website Fingerprinting (WF) attacks identify the websites visited by users by performing traffic analysis, compromising user privacy. Particularly, DL-based WF attacks demonstrate impressive attack performance. However, the effectiveness of DL-based WF attacks relies on the collected complete and pure traffic during the page loading, which impacts the practicality of these attacks. The WF performance is rather low under dynamic network conditions and various WF defenses, particularly when the analyzed traffic is only a small part of the complete traffic. In this paper, we propose Holmes, a robust and reliable early-stage WF attack. Holmes utilizes temporal and spatial distribution analysis of website traffic to effectively identify websites in the early stages of page loading. Specifically, Holmes develops adaptive data augmentation based on the temporal distribution of website traffic and utilizes a supervised contrastive learning method to extract the correlations between the early-stage traffic and the pre-collected complete traffic. Holmes accurately identifies traffic in the early stages of page loading by computing the correlation of the traffic with the spatial distribution information, which ensures robust and reliable detection according to early-stage traffic. We extensively evaluate Holmes using six datasets. Compared to nine existing DL-based WF attacks, Holmes improves the F1-score of identifying early-stage traffic by an average of 169.18%. Furthermore, we replay the traffic of visiting real-world dark web websites. Holmes successfully identifies dark web websites when the ratio of page loading on average is only 21.71%, with an average precision improvement of 169.36% over the existing WF attacks.