https://github.com/bcebere/understanding-and-explaining-web-fingerprinting-with-a-protocol-centric-approach
Website Fingerprinting from a Protocol-Centric Perspective
https://github.com/bcebere/understanding-and-explaining-web-fingerprinting-with-a-protocol-centric-approach
encrypted-traffic-analysis encrypted-traffic-classification machine-learning traffic-analysis website-fingerprinting
Last synced: about 2 months ago
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Website Fingerprinting from a Protocol-Centric Perspective
- Host: GitHub
- URL: https://github.com/bcebere/understanding-and-explaining-web-fingerprinting-with-a-protocol-centric-approach
- Owner: bcebere
- License: mit
- Created: 2023-12-06T10:09:43.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-03T06:30:33.000Z (about 1 year ago)
- Last Synced: 2025-03-11T06:45:58.202Z (7 months ago)
- Topics: encrypted-traffic-analysis, encrypted-traffic-classification, machine-learning, traffic-analysis, website-fingerprinting
- Language: Python
- Homepage:
- Size: 17.8 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Understanding-and-Explaining-Web-Fingerprinting-with-a-Protocol-Centric-Approach
In this repository, we provide the code to reproduce the results in the "Understanding-and-Explaining-Web-Fingerprinting-with-a-Protocol-Centric-Approach" paper.
The repository includes reference Machine Learning models for evaluation in the `models` folder and tools for generating HTTPS datasets in the `crawlers` folder.
The code was tested using `Linux Mint 21.2 Victoria`, `Python 3.10`, and `selenium/standalone-chrome:120.0-20231212`.
## Repository structure
This repository is organized as follows:
```bash
models/
|- src/ # Models and evaluation methods
|- tests/ # Unit tests for the ML models
crawlers/
|- src/ # Traffic crawling and parsing
|- tests/ # Unit tests for the crawling logic
experiments/
|- domains_experiments/ # Domain experiments
|- crawler/ # Domain crawling logic
|- scripts/ # Domain fingerprinting evaluation
|- page_wiki_experiments/ # Wikipedia experiments
|- crawler/ # Wikipedia crawling logic
|- scripts/ # Wikipedia fingerprinting evaluation
|- page_9gag_experiments/ # 9GAG experiments
|- crawler/ # Wikipedia crawling logic
|- scripts/ # Wikipedia fingerprinting evaluation
|- page_imdb_experiments/ # IMDB experiments
|- crawler/ # Wikipedia crawling logic
|- scripts/ # Wikipedia fingerprinting evaluation
```
## Models
The evaluation models are located in the `models`, and they are organized in a standalone library.
The library can be installed using
```bash
cd models/
pip install -e .
pip install -e .[testing] # for the development setup
```
### Usage examples:
XGBoost
```python
# XGBoost usage and evaluation example
from sklearn.datasets import load_iris
# tls_fingerprinting absolute
from tls_fingerprinting.models.base.static.xgb import XGBoostClassifier as model
from tls_fingerprinting.utils.evaluation import evaluate_classifier
test_plugin = model()
X, y = load_iris(return_X_y=True, as_frame=True)
scores = evaluate_classifier(test_plugin, X, y)
print(scores["str"])
# Example Output
# {'aucroc_ovo_macro': '0.9832 +/- 0.004', 'aucroc_ovr_micro': '0.9841 +/- 0.008', 'aucroc_ovr_weighted': '0.9832 +/- 0.004', 'aucprc_weighted': '0.9766 +/- 0.005', 'aucprc_macro': '0.9766 +/- 0.005', 'aucprc_micro': '0.9766 +/- 0.005', 'accuracy': '0.9333 +/- 0.021', 'f1_score_micro': '0.9333 +/- 0.021', 'f1_score_macro': '0.933 +/- 0.021', 'f1_score_weighted': '0.9331 +/- 0.022', 'kappa': '0.9 +/- 0.032', 'kappa_quadratic': '0.9501 +/- 0.016', 'precision_micro': '0.9333 +/- 0.021', 'precision_macro': '0.933 +/- 0.021', 'precision_weighted': '0.9333 +/- 0.021', 'recall_micro': '0.9333 +/- 0.021', 'recall_macro': '0.9334 +/- 0.021', 'recall_weighted': '0.9333 +/- 0.021', 'mcc': '0.9002 +/- 0.032'}
```
Neural Nets
```python
# MLP usage and evaluation example
from sklearn.datasets import load_iris
import numpy as np
from tls_fingerprinting.models.base.nn.mlp import MLP as model
from tls_fingerprinting.utils.evaluation import evaluate_classifier
X, y = load_iris(return_X_y=True, as_frame=True)
test_plugin = model(
task_type="classification",
n_units_in=X.shape[1],
n_units_out=len(np.unique(y)),
)
scores = evaluate_classifier(test_plugin, X, y)
print(scores["str"])
# Example Output
# {'aucroc_ovo_macro': '0.9791 +/- 0.012', 'aucroc_ovr_micro': '0.9672 +/- 0.024', 'aucroc_ovr_weighted': '0.9787 +/- 0.013', 'aucprc_weighted': '0.9496 +/- 0.034', 'aucprc_macro': '0.9496 +/- 0.034', 'aucprc_micro': '0.9496 +/- 0.034', 'accuracy': '0.8667 +/- 0.087', 'f1_score_micro': '0.8667 +/- 0.087', 'f1_score_macro': '0.8559 +/- 0.104', 'f1_score_weighted': '0.8555 +/- 0.104', 'kappa': '0.8008 +/- 0.13', 'kappa_quadratic': '0.9081 +/- 0.052', 'precision_micro': '0.8667 +/- 0.087', 'precision_macro': '0.9025 +/- 0.039', 'precision_weighted': '0.9038 +/- 0.036', 'recall_micro': '0.8667 +/- 0.087', 'recall_macro': '0.8685 +/- 0.085', 'recall_weighted': '0.8667 +/- 0.087', 'mcc': '0.8235 +/- 0.098'}
```
### Tests
```bash
pytest -vvsx
```
## Traffic Crawlers
The `crawlers` folder contains scripts for generating and parsing PCAP files from lists of URLS.
### Library Installation
```bash
cd crawlers
pip install -e .
pip install -e .[testing]
```
### Docker Build
The experiments use a custom Selenium Docker image, with additional scripts and features. Tu build the images, run
```bash
cd crawlers/docker
docker build --tag selenium-chrome -f Dockerfile_chrome .
docker build --tag selenium-firefox -f Dockerfile_firefox .
```
### Usage example
See [dataset crawlers](experiments/domains_experiments/crawler/crawl.py).
### Tests
If the library and docker builds worked, the unit tests should pass
```bash
cd crawlers
pytest -vvsx
```
## Experiments
The experiments are available in the `experiments` folder. Each experiment includes the crawling scripts and the fingerprinting evaluation code. The 9GAG and IMDB are not included in the repository due size.
```bash
experiments/
|- domains_experiments/ # Domain experiments
|- crawler/ # Domain crawling logic
|- scripts/ # Domain fingerprinting evaluation
|- page_wiki_experiments/ # Wikipedia experiments
|- crawler/ # Wikipedia crawling logic
|- scripts/ # Wikipedia fingerprinting evaluation
|- page_9gag_experiments/ # 9GAG experiments
|- crawler/ # Wikipedia crawling logic
|- scripts/ # Wikipedia fingerprinting evaluation
|- page_imdb_experiments/ # IMDB experiments
|- crawler/ # Wikipedia crawling logic
|- scripts/ # Wikipedia fingerprinting evaluation
```
## Citing
If you use this code, please cite the associated paper:
```
@inproceedings{cebere2024understanding,
title={Understanding Web Fingerprinting with a Protocol-Centric Approach},
author={Cebere, Bogdan and Rossow, Christian},
booktitle={Proceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses},
year={2024}
}
```