🎯 Phishing URL Detection using Machine Learning

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📌 Overview -

Phishing is one of the most common cyber-attacks targeting users via malicious links. This project leverages various machine learning algorithms to build a predictive model that detects whether a given URL is phishing or legitimate.

⚠️ Real-time web security is critical. This project uses intelligent systems to contribute to safer internet browsing.

Url - https://fake-url-detection-model-2.onrender.com

[ Web App Interface ]

[ Result ]

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🧠 Models Used

The following models were trained and evaluated using a labeled dataset of phishing and legitimate URLs:

Algorithm	Accuracy	Precision	Recall	F1 Score
✅ Random Forest	97.21%	0.97	0.97	0.97
✅ Decision Tree	93.11%	0.93	0.93	0.93
✅ Logistic Regression	91.78%	0.92	0.92	0.92
✅ K-Nearest Neighbors	89.92%	0.90	0.89	0.89
✅ Gaussian NB	87.68%	0.88	0.88	0.88
✅ SVM	94.21%	0.94	0.94	0.94

🏆 Random Forest was the top-performing model and selected for final deployment.

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🛠️ Tech Stack

Component	Tech Used
👩‍💻 Programming	Python 3.9+
📚 Libraries	Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn
🔍 ML Algorithms	Random Forest, Decision Tree, Logistic Regression, KNN, SVM, Gaussian NB
📁 Dataset	Public phishing URL dataset from Kaggle/UCI
📓 Environment	Jupyter Notebook

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Url : - https://fake-url-detection-model-2.onrender.com

Installation

1. Clone the repository:

   git clone https://github.com/avadheshgithub/Fake_URL_Detection-Model.git
   

2. Navigate to the project directory:

   cd Phishing-URL-Detection
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Run the application:

   python app.py
   

Directory Tree

├── pickle
│   ├── model.pkl
├── static
    ├──  Images
│      ├── Interface.png
       ├── Result.png
│   ├── styles.css
├── templates
│   ├── index.html
    ├── Result.html
├── Phishing URL Detection.ipynb
├── Procfile
├── README.md
├── app.py
├── feature.py
├── phishing.csv
├── requirements.txt

Technologies Used

1. Python/Flask
2. Numpy
3. Pandas
4. Matplotlib
5. Scikit learn
6. VS Code

Result

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

Conclusion

Our Project/system is ready to use

All the best | Thank you