Enhanced Phishing Detection using Machine Learning, NLP and Emerging Technologies

Phishing continues to be a prevalent cybersecurity threat that exploits fake emails, websites, or messages to steal critical and sensitive information. Traditional methods of phishing detection rely quite heavily on static methods of detection that have no adaption mechanisms to match cyber criminal’s evolving tactics. This article presented the work on developing PhishDefender-X, a hybrid phishing detection model with a range of components; including Machine Learning (ML), Natural Language Processing (NLP), Graph Analysis, Blockchain, Federated Learning, Adversarial Detection and Explainable AI (XAI). PhishDefender-X combined both ML and NLP to classify phishing email’s content and web page structure, with Graph Analysis to determine links between malicious domains with limited profiling. Blockchain was used to confirm the authentication of websites that reduced domain spoofing. Federated Learning was implemented to allow training without moving data, while ensuring data privacy, Adversarial Detection improved the model’s robustness against evasion techniques, while XAI helped provide human-readable, interpretable results. PhishDefender-X was then evaluated on real-world phishing datasets which found improvements on accuracy, false positives and strong adaptability to emerging threats providing a sound, intelligent, and useful phishing detection techniques for the next generation of phishing detection.