Website-Fingerprinting-Library (WFlib)

WFlib is a Pytorch-based open-source library for website fingerprinting attacks, intended for research purposes only.

We provide a neat code base to evaluate 11 advanced DL-based WF attacks on multiple datasets. This library is derived from our ACM CCS 2024 paper. If you find this repo useful, please cite our paper.

@inproceedings{deng2024wflib,
  title={Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis},
  author={Deng, Xinhao and Li, Qi and Xu, Ke},
  booktitle={Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security},
  year={2024}
}

We will continue to add new attacks. Contributions via pull requests are welcome and appreciated.

WFlib Overview

The code library includes 11 DL-based website fingerprinting attacks.

Attacks	Conference	Paper	Code
AWF	NDSS 2018	Automated Website Fingerprinting through Deep Learning	DLWF
DF	CCS 2018	Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning	df
Tik-Tok	PETS 2019	Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks	Tik_Tok
Var-CNN	PETS 2019	Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning	Var-CNN
TF	CCS 2019	Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning	tf
BAPM	ACSAC 2021	BAPM: Block Attention Profiling Model for Multi-tab Website Fingerprinting Attacks on Tor	None
ARES	S&P 2023	Robust Multi-tab Website Fingerprinting Attacks in the Wild	Multitab-WF-Datasets
RF	Security 2023	Subverting Website Fingerprinting Defenses with Robust Traffic Representation	RF
NetCLR	CCS 2023	Realistic Website Fingerprinting By Augmenting Network Trace	Realistic-Website-Fingerprinting-By-Augmenting-Network-Traces
TMWF	CCS 2023	Transformer-based Model for Multi-tab Website Fingerprinting Attack	TMWF
Holmes	CCS 2024	Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis	WFlib

We implemented all attacks using the same framework (Pytorch) and a consistent coding style, enabling researchers to evaluate and compare existing attacks easily.

Usage

Install

pip install --user .

Note

Python 3.8 is required.

Datasets

Download datasets (link) and place it in the folder ./datasets
Divide the dataset into training, validation, and test sets. For example, you can execute the following command.

python exp/dataset_process/dataset_split.py --dataset DF18

Training & Evaluation

We provide all experiment scripts for WF attacks in the folder ./scripts/. For example, you can reproduce the AWF attack on the DF18 dataset by executing the following command.

bash scripts/AWF.sh

The ./scripts/AWF.sh file contains the commands for model training and evaluation.

python -u exp/train.py \
  --dataset DF18 \
  --model AWF \
  --gpu 0 \
  --feature DIR \
  --seq_len 3000 \
  --train_epochs 30 \
  --batch_size 256 \
  --learning_rate 8e-4 \
  --optimizer RMSprop \
  --eval_metrics Accuracy Precision Recall F1-score P@min \
  --save_metric F1-score \
  --save_name max_f1

python -u exp/test.py \
  --dataset DF18 \
  --model AWF \
  --gpu 0 \
  --feature DIR \
  --seq_len 3000 \
  --batch_size 256 \
  --eval_metrics Accuracy Precision Recall F1-score P@min \
  --save_name max_f1

The meanings of all parameters can be found in the exp/train.py and exp/test.py files. WFlib supports modifying parameters to easily implement different attacks. Moreover, you can use WFlib to implement combinations of different attacks or perform ablation analysis.

Contact

If you have any questions or suggestions, feel free to contact:

Xinhao Deng (dengxh23@mails.tsinghua.edu.cn)

Acknowledgements

We would like to thank all the authors of the referenced papers. Special thanks to Yixiang Zhang and Jie Yan for their participation in the code review.

websitefingerprinting/Website-Fingerprinting-Library