Evaluation and Performance of a Phishing Electronic Mail Detection Model

Phishing remains a critical cybersecurity threat, deceiving users by imitating legitimate communications. This research develops a robust phishing website detection system utilizing advanced machine learning techniques. The system employs the Random Forest algorithm and ensemble methods to ensure high accuracy and resilience against adversarial attacks. Key innovations include integrating contextual information, such as sender-recipient relationships and email content, to enhance threat assessment accuracy. Additionally, the research analyses temporal patterns in email communications to identify phishing attempts before they fully manifest. The system also includes real-time threat monitoring to adapt to new phishing tactics dynamically. the work extensive evaluations confirm the model’s effectiveness, demonstrating superior performance in accuracy, precision, and recall across various test scenarios. This advancement paves the way for more secure email communication, enhancing defences against evolving phishing tactics and reducing the risk of successful phishing attacks. The Temporal Patterns, the Random Forest model’s moderate scores. The combination of Random Forest, GridSearchCV, and TF-IDF Vectorizer achieves high accuracy (0.916666) and precision (0.83), however, the ensemble method, improves performance across all metrics, and F1 Scores.