Advanced Techniques in Phishing Detection Machine Learning Approaches and Their Effectiveness

Phishing attacks continue to pose a significant threat to individuals, organizations, and digital infrastructure, evolving in complexity and scale. Traditional rule-based detection systems are increasingly insufficient against sophisticated phishing techniques. This paper explores advanced machine learning approaches for phishing detection, including supervised, unsupervised, and deep learning models. Key methodologies such as decision trees, support vector machines (SVM), random forests, neural networks, and ensemble techniques are examined in terms of accuracy, scalability, and adaptability. Feature extraction from URLs, email content, and website behavior is discussed, along with the importance of real-time detection and adversarial robustness. The study also reviews publicly available datasets and evaluates the performance of various models using metrics like precision, recall, F1-score, and ROC-AUC. The findings suggest that hybrid and deep learning approaches offer superior detection capabilities but require careful tuning and robust training data. Future directions include the integration of real-time threat intelligence and the development of models resistant to evolving phishing tactics.