Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems
A new research paper explores how concept drift affects machine learning models used for detecting phishing emails. The study aims to evaluate the performance degradation of these systems as phishing tactics evolve and to propose mitigation strategies. The paper highlights the increasing sophistication of phishing attacks and the critical role of email spam filters in protecting users. AI
IMPACT Addresses the challenge of maintaining effective AI-based security systems against evolving threats.