# Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning

arXiv: https://arxiv.org/abs/1801.02265 · HTML: https://mlsanity.com/paper/1801.02265/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2018.

## Built on by (most cited first, 30 of 43)

- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification](https://mlsanity.com/paper/2202.06335/) (2022)
- [NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging](https://mlsanity.com/paper/2310.04429/) (2023)
- [TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification](https://mlsanity.com/paper/2307.16713/) (2023)
- [Remote Timing Attacks on Efficient Language Model Inference](https://mlsanity.com/paper/2410.17175/) (2024)
- [Realistic Website Fingerprinting By Augmenting Network Trace](https://mlsanity.com/paper/2309.10147/) (2023)
- [Exposing LLM User Privacy via Traffic Fingerprint Analysis: A Study of Privacy Risks in LLM Agent Interactions](https://mlsanity.com/paper/2510.07176/) (2025)
- [Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis](https://mlsanity.com/paper/2407.00918/) (2024)
- [Lens: A Knowledge-Guided Foundation Model for Network Traffic](https://mlsanity.com/paper/2402.03646/) (2024)
- [TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning](https://mlsanity.com/paper/2412.11471/) (2024)
- [Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic](https://mlsanity.com/paper/2302.13763/) (2023)
- [Mapping the Landscape of Generative AI in Network Monitoring and Management](https://mlsanity.com/paper/2502.08576/) (2025)
- [Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification](https://mlsanity.com/paper/2505.20866/) (2025)
- [Towards Fine-Grained Webpage Fingerprinting at Scale](https://mlsanity.com/paper/2409.04341/) (2024)
- [FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classification](https://mlsanity.com/paper/2508.19924/) (2025)
- [Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2310.20469/) (2023)
- [When AI Defeats Password Deception! A Deep Learning Framework to Distinguish Passwords and Honeywords](https://mlsanity.com/paper/2407.16964/) (2024)
- [Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model](https://mlsanity.com/paper/2501.03279/) (2025)
- [STAR: Semantic-Traffic Alignment and Retrieval for Zero-Shot HTTPS Website Fingerprinting](https://mlsanity.com/paper/2512.17667/) (2025)
- [ResAware: Cross-Environment Website Fingerprinting via Resource-Privileged Distillation](https://mlsanity.com/paper/2606.17462/) (2026)
- [NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks](https://mlsanity.com/paper/2202.09873/) (2022)
- [SiamHAN: IPv6 Address Correlation Attacks on TLS Encrypted Traffic via Siamese Heterogeneous Graph Attention Network](https://mlsanity.com/paper/2204.09465/) (2022)
- [One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning](https://mlsanity.com/paper/2402.07501/) (2024)
- [Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification](https://mlsanity.com/paper/2407.19687/) (2024)
- [TrafficMoE: Heterogeneity-aware Mixture of Experts for Encrypted Traffic Classification](https://mlsanity.com/paper/2603.29520/) (2026)
- [Mean Masked Autoencoder with Flow-Mixing for Encrypted Traffic Classification](https://mlsanity.com/paper/2603.29537/) (2026)
- [UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts](https://mlsanity.com/paper/2605.17575/) (2026)
- [From Privacy to Workflow Integrity: Communication-Graph Metadata in Autonomous Agent Interoperability](https://mlsanity.com/paper/2606.07150/) (2026)
- [CipherSight: Robust Website Fingerprinting via Record-Resource Semantic Supervision under Distribution Shifts](https://mlsanity.com/paper/2608.13905/) (2026)
- [Pruned Traffic Trees: Native Semantic Compression with a Protocol-Structured Model Family for Encrypted Traffic Classification](https://mlsanity.com/paper/2608.21874/) (2026)

## Abstract (arXiv)

Website fingerprinting enables a local eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting attacks have been shown to be effective even against Tor. Recently, lightweight website fingerprinting defenses for Tor have been proposed that substantially degrade existing attacks: WTF-PAD and Walkie-Talkie. In this work, we present Deep Fingerprinting (DF), a new website fingerprinting attack against Tor that leverages a type of deep learning called Convolutional Neural Networks (CNN) with a sophisticated architecture design, and we evaluate this attack against WTF-PAD and Walkie-Talkie. The DF attack attains over 98% accuracy on Tor traffic without defenses, better than all prior attacks, and it is also the only attack that is effective against WTF-PAD with over 90% accuracy. Walkie-Talkie remains effective, holding the attack to just 49.7% accuracy. In the more realistic open-world setting, our attack remains effective, with 0.99 precision and 0.94 recall on undefended traffic. Against traffic defended with WTF-PAD in this setting, the attack still can get 0.96 precision and 0.68 recall. These findings highlight the need for effective defenses that protect against this new attack and that could be deployed in Tor.