# Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks

arXiv: https://arxiv.org/abs/1902.06421 · HTML: https://mlsanity.com/paper/1902.06421/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2019.

## Built on by (most cited first, 15 of 15)

- [Realistic Website Fingerprinting By Augmenting Network Trace](https://mlsanity.com/paper/2309.10147/) (2023)
- [Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification](https://mlsanity.com/paper/2205.05628/) (2022)
- [Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis](https://mlsanity.com/paper/2407.00918/) (2024)
- [TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning](https://mlsanity.com/paper/2412.11471/) (2024)
- [Towards Fine-Grained Webpage Fingerprinting at Scale](https://mlsanity.com/paper/2409.04341/) (2024)
- [ResAware: Cross-Environment Website Fingerprinting via Resource-Privileged Distillation](https://mlsanity.com/paper/2606.17462/) (2026)
- [Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning](https://mlsanity.com/paper/2506.03207/) (2025)
- [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)
- [A Large-Scale Benchmark and Risk Assessment of Traffic Analysis Attacks on Cloud LLM Services](https://mlsanity.com/paper/2609.31877/) (2026)
- [Examining the Rat in the Tunnel: Interpretable Multi-Label Classification of Tor-based Malware](https://mlsanity.com/paper/2409.16639/) (2024)
- [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)
- [Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection](https://mlsanity.com/paper/2604.08800/) (2026)
- [FlowAtom: Atom-Based Evidence Aggregation for Multi-Label Website Fingerprinting](https://mlsanity.com/paper/2609.29330/) (2026)

## Abstract (arXiv)

A passive local eavesdropper can leverage Website Fingerprinting (WF) to deanonymize the web browsing activity of Tor users. The value of timing information to WF has often been discounted in recent works due to the volatility of low-level timing information. In this paper, we more carefully examine the extent to which packet timing can be used to facilitate WF attacks. We first propose a new set of timing-related features based on burst-level characteristics to further identify more ways that timing patterns could be used by classifiers to identify sites. Then we evaluate the effectiveness of both raw timing and directional timing which is a combination of raw timing and direction in a deep-learning-based WF attack. Our closed-world evaluation shows that directional timing performs best in most of the settings we explored, achieving: (i) 98.4% in undefended Tor traffic; (ii) 93.5% on WTF-PAD traffic, several points higher than when only directional information is used; and (iii) 64.7% against onion sites, 12% higher than using only direction. Further evaluations in the open-world setting show small increases in both precision (+2%) and recall (+6%) with directional-timing on WTF-PAD traffic. To further investigate the value of timing information, we perform an information leakage analysis on our proposed handcrafted features. Our results show that while timing features leak less information than directional features, the information contained in each feature is mutually exclusive to one another and can thus improve the robustness of a classifier.