Phishing Website Detection via ML Enhancement

Detection of Phishing Website Using Machine Learning we have to add Feature Engineering Upgrades
- Add content-based features like HTML tag patterns, JavaScript behavior, and embedded media analysis.
- Use real-time WHOIS data for domain age and registration info.
- Include SSL certificate validation and favicon analysis.

2. Advanced ML Techniques
- Try ensemble models like Random Forest + AdaBoost for better accuracy.
- Explore deep learning with LSTM or CNN if you're analyzing webpage content or sequences.
- Implement feature selection algorithms (e.g., PCA or Recursive Feature Elimination) to optimize performance.

3. Dataset Improvements
- Use live data scraping from sources like PhishTank and Tranco to build a fresh dataset.
- Balance the dataset using SMOTE or other oversampling techniques to avoid bias.

4. Real-Time Detection System
- Build a web-based tool or browser extension that flags phishing sites in real time.
- Integrate with APIs to scan URLs dynamically before users click them.

5. Explainability & Visualization
- Use SHAP or LIME to explain model predictions.
- Create dashboards with Streamlit or Dash to visualize detection results and model performance.

6. Security & Ethical Angle
- Add a module to detect phishing via QR codes or shortened URLs.
- Discuss ethical implications and user privacy in your report.