Multi-Modal Phishing Detection Using PCA, Random Forest, and XGBoost

Phishing attacks have evolved beyond deceptive URLs to include malicious emails, QR codes, images, and text, posing serious cybersecurity threats. This research presents a multi-modal phishing detection framework that leverages Princi- pal Component Analysis (PCA) for dimensionality reduction and ensemble learning models—Random Forest and XGBoost—for accurate classification of fraudulent content. The system analyzes diverse inputs, including URLs, QR codes, and textual data, stored and processed through serialized .pkl datasets. Imple- mented using Python and Flask, the framework integrates Ex- plainable AI techniques (SHAP, LIME) for model transparency. Experimental results demonstrate high accuracy, efficiency, and interpretability, offering a scalable solution for detecting phishing threats across multiple digital communication channels.