https://github.com/tristanbilot/phishgnn
Phishing detection using GNNs (SECRYPT'22)
https://github.com/tristanbilot/phishgnn
gnn graphs phishing-detection representation-learning
Last synced: 9 months ago
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Phishing detection using GNNs (SECRYPT'22)
- Host: GitHub
- URL: https://github.com/tristanbilot/phishgnn
- Owner: TristanBilot
- License: mit
- Created: 2022-03-23T10:54:27.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-06-06T07:46:06.000Z (about 1 year ago)
- Last Synced: 2025-09-09T14:12:14.249Z (10 months ago)
- Topics: gnn, graphs, phishing-detection, representation-learning
- Language: Rust
- Homepage:
- Size: 33.6 MB
- Stars: 14
- Watchers: 3
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# PhishGNN
Code for the paper: [PhishGNN: A Phishing Website Detection Framework using Graph Neural Networks](https://hal.science/hal-04401167v1/file/PhishGNN_A_Phishing_Website_Detection_Framework_using_Graph_Neural_Networks.pdf).
## Installation
### Clone the repo
```
git clone https://github.com/TristanBilot/phishGNN.git
cd phishGNN
```
### Install dependencies
```python
python3 -m venv venv
. venv/bin/activate
pip install wheel
pip install -r requirements.txt
pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cpu.html # for cpu
```
### unzip the dataset
```shell
./install_dataset.sh
```
## Dataset & crawler
The dataset can be downloaded in PyG format and new features can be extracted from URLs using the crawler.
A full guide for both tasks can be found here.
## Training
During training, the files located in data/training/processed will be used by default. The raw dataset is composed of urls mapped to around 30 features, including a list of references (href, form, iframe) to other pages, which also have their own features and their list of references.
```
python phishGNN/training.py
```
## Visualize node embeddings
During training, it is possible to generate the embeddings just after passing through the Graph Convolutional layers. Just run the training with the following option:
```
python phishGNN/training.py --plot-embeddings
```
## Visualize the graphs
A tool has been developed in order to visualize graphically the internal structure of web pages from the dataset along with their characteristics such as the number of nodes/edges and whether the page is phishing or benign.
To visualize these data, first follow the instructions in the installation part, run the `visualization` script and open the file `visualization/visualization.html`.
```bash
python visualization.py
```
## Citation
If you use this code, please cite the following paper.
```
@inproceedings{bilot2022phishgnn,
title={Phishgnn: a phishing website detection framework using graph neural networks},
author={Bilot, Tristan and Geis, Gr{\'e}goire and Hammi, Badis},
booktitle={19th International Conference on Security and Cryptography},
pages={428--435},
year={2022},
organization={SCITEPRESS-Science and Technology Publications}
}
```
## License
MIT