Real-Time Phishing Attack Detection through Advanced Machine Learning Techniques

The proposed method, “EnsemblePhish,” revolutionizes real-time phishing attack detection through a comprehensive multi-step approach. It involves data collection, preprocessing, feature engineering, model selection, ensemble techniques, and real-time detection. The process begins with collecting and preprocessing a labeled dataset (D) containing phishing and non-phishing instances. Feature engineering (F) extracts crucial characteristics through URL analysis, content analysis, and behavior analysis. Three distinct machine learning algorithms—Random Forest, Support Vector Machines (SVM), and a Deep Neural Network (DNN)—are chosen for training based on the extracted features. These models are combined through an ensemble method to enhance performance. Real-time detection involves continuous monitoring of network traffic, extracting real-time features, and employing the ensemble model for swift phishing prediction. The decision made leads to appropriate alerts, fortifying cybersecurity.