arXiv AI September 10, 2026 By Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti

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

CoGReV is a hybrid framework that enhances machine‑learning phishing classifiers with a post‑hoc, non‑monotonic reasoning layer written in Answer Set Programming. It uses a confidence‑gated defeasible rule to revise low‑confidence phishing predictions toward legitimate only when website metadata is available, thereby allocating uncertain decisions to the reasoning layer while leaving confident ones to the classifier. The gated rule reduces false positives by 0.27 % of decisions and maintains recall within 0.7 % of the baseline, operating in linear time.

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arXiv AI

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One email a morning, machine-written

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