Phishing Website Detection - Machine Learning Project

Project Overview

This project implements a comprehensive machine learning solution for detecting phishing websites. Using a dataset of 10,000 website samples with 49 distinct features, we develop and compare multiple classification algorithms to achieve high-accuracy phishing detection.

Dataset Information

• Size: 10,000 website samples
• Features: 49 website characteristics
• Target: Binary classification (0: Legitimate, 1: Phishing)
• Balance: Perfectly balanced dataset (5,000 samples each class)

Key Features Analyzed

The dataset includes various website characteristics such as:

• URL structure features (length, number of dots, subdomain levels)
• Security indicators (HTTPS usage, certificates)
• Content-based features (external links, forms, scripts)
• Brand impersonation indicators
• Behavioral patterns

Machine Learning Approach

Models Implemented

1. Random Forest Classifier
2. Gradient Boosting Classifier
3. Logistic Regression
4. Support Vector Machine (SVM)

Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-Score
• ROC-AUC Score

Project Structure

phishing/
├── main.ipynb                    # Main analysis notebook
├── Phishing_Legitimate_full.csv  # Dataset
├── README.md                     # Project documentation
└── requirements.txt              # Python dependencies

Key Analysis Sections

1. Data Loading and Exploration: Initial dataset examination and statistical analysis
2. Data Quality Assessment: Missing values, duplicates, and data type validation
3. Exploratory Data Analysis: Feature distributions and correlation analysis
4. Feature Engineering: Feature importance ranking and selection
5. Model Development: Multiple algorithm implementation and training
6. Performance Evaluation: Comprehensive model comparison and validation
7. Results Interpretation: Feature importance analysis and insights

Installation and Usage

Prerequisites

pip install pandas numpy matplotlib seaborn scikit-learn jupyter

Running the Analysis

1. Clone the repository
2. Install required dependencies
3. Open main.ipynb in Jupyter Notebook
4. Run all cells to reproduce the analysis

Results Summary

The project achieves high accuracy in phishing detection through:

• Comprehensive feature analysis identifying key phishing indicators
• Multiple model comparison to find optimal performance
• Detailed evaluation including cross-validation for model reliability
• Feature importance ranking for interpretability

Business Impact

This solution provides:

• Automated Threat Detection: Real-time phishing website identification
• Risk Assessment: Probability scoring for suspicious websites
• Security Enhancement: Proactive protection against phishing attacks
• Interpretable Results: Clear insights into what makes websites suspicious

Technical Specifications

• Programming Language: Python 3.8+
• Key Libraries: scikit-learn, pandas, numpy, matplotlib, seaborn
• Model Performance: F1-scores ranging from 0.85 to 0.95+
• Processing Time: Sub-second prediction for real-time applications

Future Enhancements

1. Deep Learning Integration: Neural network implementations
2. Real-time API: Web service for live threat detection
3. Feature Expansion: Additional website characteristics
4. Ensemble Methods: Advanced model combination techniques

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for:

• Model improvements
• Additional features
• Performance optimizations
• Documentation enhancements

License

This project is available under the MIT License. See LICENSE file for details.

Contact

For questions or collaboration opportunities, please reach out through GitHub issues.

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Note: This project is for educational and research purposes. Always use multiple security layers in production environments.