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New research tackles evolving phishing tactics impacting ML detection

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.

RANK_REASON Academic paper on ML for security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Warren Fernando, Nikos Komninos ·

    Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems

    arXiv:2606.11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professio…