# Realistic Website Fingerprinting By Augmenting Network Trace

arXiv: https://arxiv.org/abs/2309.10147 · HTML: https://mlsanity.com/paper/2309.10147/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 18 Sep 2023.

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

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning](https://mlsanity.com/paper/1606.04586/) (2016)
- [Learning with Pseudo-Ensembles](https://mlsanity.com/paper/1412.4864/) (2014)
- [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)
- [Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks](https://mlsanity.com/paper/1902.06421/) (2019)
- [DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users](https://mlsanity.com/paper/1307.1542/) (2013)

## Built on by (most cited first, 10 of 10)

- [Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis](https://mlsanity.com/paper/2407.00918/) (2024)
- [Towards Fine-Grained Webpage Fingerprinting at Scale](https://mlsanity.com/paper/2409.04341/) (2024)
- [STAR: Semantic-Traffic Alignment and Retrieval for Zero-Shot HTTPS Website Fingerprinting](https://mlsanity.com/paper/2512.17667/) (2025)
- [UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts](https://mlsanity.com/paper/2605.17575/) (2026)
- [Treat Traffic Like Trees: A Semantic-Preserving Hierarchical Graph-Based Expert Framework for Encrypted Traffic Analysis](https://mlsanity.com/paper/2606.04517/) (2026)
- [A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services](https://mlsanity.com/paper/2609.31877/) (2026)
- [Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks](https://mlsanity.com/paper/2506.20082/) (2025)
- [Beyond a Single Perspective: Towards a Realistic Evaluation of Website Fingerprinting Attacks](https://mlsanity.com/paper/2510.14283/) (2025)
- [BEACON: A Multimodal Dataset for Learning Behavioral Fingerprints from Gameplay Data](https://mlsanity.com/paper/2605.10867/) (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 (WF) is considered a major threat to the anonymity of Tor users (and other anonymity systems). While state-of-the-art WF techniques have claimed high attack accuracies, e.g., by leveraging Deep Neural Networks (DNN), several recent works have questioned the practicality of such WF attacks in the real world due to the assumptions made in the design and evaluation of these attacks. In this work, we argue that such impracticality issues are mainly due to the attacker's inability in collecting training data in comprehensive network conditions, e.g., a WF classifier may be trained only on samples collected on specific high-bandwidth network links but deployed on connections with different network conditions. We show that augmenting network traces can enhance the performance of WF classifiers in unobserved network conditions. Specifically, we introduce NetAugment, an augmentation technique tailored to the specifications of Tor traces. We instantiate NetAugment through semi-supervised and self-supervised learning techniques. Our extensive open-world and close-world experiments demonstrate that under practical evaluation settings, our WF attacks provide superior performances compared to the state-of-the-art; this is due to their use of augmented network traces for training, which allows them to learn the features of target traffic in unobserved settings. For instance, with a 5-shot learning in a closed-world scenario, our self-supervised WF attack (named NetCLR) reaches up to 80% accuracy when the traces for evaluation are collected in a setting unobserved by the WF adversary. This is compared to an accuracy of 64.4% achieved by the state-of-the-art Triplet Fingerprinting [35]. We believe that the promising results of our work can encourage the use of network trace augmentation in other types of network traffic analysis.