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https://github.com/Xinhao-Deng/Website-Fingerprinting-Library
A Library for Advanced DL-based Website Fingerprinting Attacks.
https://github.com/Xinhao-Deng/Website-Fingerprinting-Library
Last synced: about 2 months ago
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A Library for Advanced DL-based Website Fingerprinting Attacks.
- Host: GitHub
- URL: https://github.com/Xinhao-Deng/Website-Fingerprinting-Library
- Owner: Xinhao-Deng
- License: mit
- Created: 2024-05-27T08:22:07.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2024-12-07T03:57:03.000Z (2 months ago)
- Last Synced: 2024-12-07T04:25:07.088Z (2 months ago)
- Language: Python
- Size: 675 KB
- Stars: 61
- Watchers: 1
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-fingerprinting - Website Fingerprinting Library - based open-source library for website fingerprinting attacks | (Libraries & Projects / Index)
- awesome-fingerprinting - Website Fingerprinting Library - based open-source library for website fingerprinting attacks | (Libraries & Projects / Index)
README
# Website-Fingerprinting-Library (WFlib)
WFlib is a Pytorch-based open-source library for website fingerprinting attacks, intended for research purposes only.
Website fingerprinting is a type of network attack in which an adversary attempts to deduce which website a user is visiting based on encrypted traffic patterns, even without directly seeing the content of the traffic.
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.
```bibtex
@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}
}
```
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](https://arxiv.org/pdf/1708.06376) | [DLWF](https://github.com/DistriNet/DLWF) |
| DF | CCS 2018 | [Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning](https://dl.acm.org/doi/pdf/10.1145/3243734.3243768) | [df](https://github.com/deep-fingerprinting/df) |
| Tik-Tok | PETS 2019 | [Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks](https://petsymposium.org/popets/2020/popets-2020-0043.pdf) | [Tik_Tok](https://github.com/msrocean/Tik_Tok) |
| Var-CNN | PETS 2019 | [Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning](https://arxiv.org/pdf/1802.10215) | [Var-CNN](https://github.com/sanjit-bhat/Var-CNN) |
| TF | CCS 2019 | [Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning](https://dl.acm.org/doi/pdf/10.1145/3319535.3354217) | [tf](https://github.com/triplet-fingerprinting/tf) |
| BAPM | ACSAC 2021 | [BAPM: Block Attention Profiling Model for Multi-tab Website Fingerprinting Attacks on Tor](https://dl.acm.org/doi/pdf/10.1145/3485832.3485891) | None |
| ARES | S&P 2023 | [Robust Multi-tab Website Fingerprinting Attacks in the Wild](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) | [Multitab-WF-Datasets](https://github.com/Xinhao-Deng/Multitab-WF-Datasets) |
| RF | Security 2023 | [Subverting Website Fingerprinting Defenses with Robust Traffic Representation](https://www.usenix.org/system/files/sec23fall-prepub-621_shen-meng.pdf) | [RF](https://github.com/robust-fingerprinting/RF) |
| NetCLR | CCS 2023 | [Realistic Website Fingerprinting By Augmenting Network Trace](https://arxiv.org/pdf/2309.10147) | [Realistic-Website-Fingerprinting-By-Augmenting-Network-Traces](https://github.com/SPIN-UMass/Realistic-Website-Fingerprinting-By-Augmenting-Network-Traces) |
| TMWF | CCS 2023 | [Transformer-based Model for Multi-tab Website Fingerprinting Attack](https://dl.acm.org/doi/abs/10.1145/3576915.3623107) | [TMWF](https://github.com/jzx-bupt/TMWF) |
| Holmes | CCS 2024 | [Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis](https://arxiv.org/pdf/2407.00918) | [WFlib](https://github.com/Xinhao-Deng/Website-Fingerprinting-Library)|
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
```sh
git clone [email protected]:Xinhao-Deng/Website-Fingerprinting-Library.git
pip install --user .
```
**Note**
- Python 3.8 is required.
### Datasets
```sh
mkdir datasets
```
- Download datasets ([link](https://zenodo.org/records/13732130)) and place it in the folder `./datasets`
| Datasets | # of monitored websites | # of instances | Intro |
| --- | --- | --- | --- |
| CW.npz | 95 | 105730 | Closed-world dataset. [Details](https://dl.acm.org/doi/pdf/10.1145/3243734.3243768)|
| OW.npz | 95 | 146446 | Open-world dataset. [Details](https://dl.acm.org/doi/pdf/10.1145/3243734.3243768) |
| WTF-PAD.npz | 95 | 105730 | Dataset with WTF-PAD defense. [Details](https://arxiv.org/pdf/1512.00524) |
| Front.npz | 95 | 95000 | Dataset with Front defense. [Details](https://www.usenix.org/system/files/sec20-gong.pdf) |
| Walkie-Talkie.npz | 100 | 90000 | Dataset with Walkie-Talkie defense. [Details](https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-wang-tao.pdf) |
| TrafficSliver.npz | 95 | 95000 | Dataset with TrafficSliver defense. [Details](https://sebastianreuter.info/publications/pdf/ccs-trafficsliver.pdf) |
| NCDrift_sup.npz | 93 | 21430 | Network condition drift dataset, including superior traces. [Details](https://arxiv.org/pdf/2309.10147) |
| NCDrift_inf.npz | 93 | 6882 | Network condition drift dataset, including inferior traces. [Details](https://arxiv.org/pdf/2309.10147) |
| Closed_2tab.npz | 100 | 58000 | 2-tab dataset in the closed-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
| Closed_3tab.npz | 100 | 58000 | 3-tab dataset in the closed-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
| Closed_4tab.npz | 100 | 58000 | 4-tab dataset in the closed-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
| Closed_5tab.npz | 100 | 58000 | 5-tab dataset in the closed-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
| Open_2tab.npz | 100 | 64000 | 2-tab dataset in the open-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
| Open_3tab.npz | 100 | 64000 | 3-tab dataset in the open-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
| Open_4tab.npz | 100 | 64000 | 4-tab dataset in the open-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
| Open_5tab.npz | 100 | 64000 | 5-tab dataset in the open-world scenario. [Details](http://www.thucsnet.com/wp-content/papers/xinhao_sp2023.pdf) |
- The extracted dataset is in npz format and contains two values: X and y. X represents the cell sequence, with values being the direction (e.g., 1 or -1) multiplied by the timestamp. y corresponds to the labels. Note that the input of some datasets consists only of direction sequences.
- Divide the dataset into training, validation, and test sets.
```sh
# For single-tab datasets
python exp/dataset_process/dataset_split.py --dataset CW
# For multi-tab datasets
python exp/dataset_process/dataset_split.py --dataset Closed_2tab --use_stratify False
```
### Training \& Evaluation
We provide all experiment scripts for WF attacks in the folder `./scripts/`. For example, you can reproduce the DF attack on the CW dataset by executing the following command.
```sh
bash scripts/DF.sh
```
The `./scripts/DF.sh` file contains the commands for model training and evaluation.
```sh
dataset=CW
python -u exp/train.py \
--dataset ${dataset} \
--model DF \
--device cuda:1 \
--feature DIR \
--seq_len 5000 \
--train_epochs 30 \
--batch_size 128 \
--learning_rate 2e-3 \
--optimizer Adamax \
--eval_metrics Accuracy Precision Recall F1-score \
--save_metric F1-score \
--save_name max_f1
python -u exp/test.py \
--dataset ${dataset} \
--model DF \
--device cuda:1 \
--feature DIR \
--seq_len 5000 \
--batch_size 256 \
--eval_metrics Accuracy Precision Recall F1-score \
--load_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](https://xinhao-deng.github.io/) ([email protected])
## Acknowledgements
We would like to thank all the authors of the referenced papers. Special thanks to **Yixiang Zhang** for his support.