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CoGReV

model1 events
papersSEP 10 04:00 UTC

CoGReV: A Confidence-Gated Post-Hoc Belief Revision Framework for Phishing Website Classification

Researchers introduce CoGReV, a framework that applies confidence-gated, non-monotonic belief revision to adjust machine learning outputs in phishing website detection. The goal is to reduce false alarms that burden human analysts reviewing classifier decisions, which can otherwise lead to alert fatigue and weaker oversight. The paper appears on arXiv as a version-3 replacement.

phishing/cybersecurity/machine-learning/classification