You Can visit to the Website phidect.me
The Internet has become an indispensable part of our life, However, It also has provided opportunities to anonymously perform malicious activities like Phishing. Phishers try to deceive their victims by social engineering or creating mockup websites to steal information such as account ID, username, password from individuals and organizations. Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. One of the most successful methods for detecting these malicious activities is Machine Learning. This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods. To see project click here.
The Code is written in Python 3.6.10. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:
1. Environment setup.conda create --prefix venv python==3.6.10 -y
conda activate venv/
- Install Requirements and setup
pip install -r requirements.txt
- Run Application
python app.py
├── pickle
│ ├── model.pkl
├── static
│ ├── styles.css
├── templates
│ ├── index.html
├── Phishing URL Detection.ipynb
├── Procfile
├── README.md
├── app.py
├── feature.py
├── phishing.csv
├── requirements.txt
Accuracy of various model used for URL detection
| ML Model | Accuracy | f1_score | Recall | Precision | |
|---|---|---|---|---|---|
| 0 | Gradient Boosting Classifier | 0.974 | 0.977 | 0.994 | 0.986 |
| 1 | CatBoost Classifier | 0.972 | 0.975 | 0.994 | 0.989 |
| 2 | XGBoost Classifier | 0.969 | 0.973 | 0.993 | 0.984 |
| 3 | Multi-layer Perceptron | 0.969 | 0.973 | 0.995 | 0.981 |
| 4 | Random Forest | 0.967 | 0.971 | 0.993 | 0.990 |
| 5 | Support Vector Machine | 0.964 | 0.968 | 0.980 | 0.965 |
| 6 | Decision Tree | 0.960 | 0.964 | 0.991 | 0.993 |
| 7 | K-Nearest Neighbors | 0.956 | 0.961 | 0.991 | 0.989 |
| 8 | Logistic Regression | 0.934 | 0.941 | 0.943 | 0.927 |
| 9 | Naive Bayes Classifier | 0.605 | 0.454 | 0.292 | 0.997 |