Detection of Phishing Websites and Emails Using Explainable Machine Learning Models

Phishing attacks pose serious cybersecurity concerns by using phony emails and websites to obtain private data. Intelligent and explicable solutions are required because traditional detection systems are unable to keep up with the latest phishing techniques. This study integrates behavioural analysis, optical character recognition (OCR), and natural language processing (NLP) to develop a phishing detection system based on Explainable Machine Learning (XML) models. The system uses supervised models, including ensemble approaches, for precise classification and anomaly detection for adaptive learning. Model transparency is improved by explainability techniques like SHAP and LIME, which help cybersecurity experts understand decisions. High detection accuracy, adaptability, and increased reliability over traditional methods are demonstrated by the experimental results. The suggested system provides a strong solution for cybersecurity resilience by improving phishing defence through real-time detection, alert systems, and adaptive learning.