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

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Updated 4 h ago · first seen 11 Sept 2026

paper_01M294GNX6W5FA3268PKCYJZKG

Published: 11 Sept 2026; T1 · 4 h ago
arXiv: 2604.25512; T1 · 4 h ago
Category: cs.AI; T1 · 4 h ago

Abstract

In phishing detection, machine learning classifiers act as a first line of defense, but the false positives they produce are triaged by human analysts. The excessive false alarms cause alert fatigue that erodes human oversight. We propose CoGReV, a hybrid framework that augments standard machine learning classifiers with a post-hoc non-monotonic reasoning layer implemented in Answer Set Programming. The layer applies a confidence-gated defeasible rule that revises a phishing prediction toward legitimate only when website metadata is present and the classifier's decision is low-confidence, deferring uncertain predictions to the reasoning layer while leaving out confident decisions to the classifiers. This gating acts as a function-allocation mechanism between the classifier and the reasoning layer. Unlike an ungated rule, which reduces false positives only by discarding genuine detections and degrades phishing recall by about nine percentage points, the proposed gated rule keeps recall within 0.7 percentage points of the no-revision baseline for all classifiers while revising only 0.27 percent of decisions. By lowering false alarms, without sacrificing detection, CoGReV reduces analysts alert fatigue and integrates new domain knowledge into the reasoning layer in $\mathcal{O}(n)$ time.

Authors 3

Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti

Specification

Official page: arxiv.org/abs/2604.25512
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
arXiv id: 2604.25512
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Categories: cs.AI
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
PDF: arxiv.org/pdf/2604.25512
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Primary category: cs.AI
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 4 h agohigh

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T19

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4 h ago

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None

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Claim history · Abstract

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Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
In phishing detection, machine learning classifiers act as a first line of defense, but the false positives they produce are triaged by human analysts. The excessive false alarms cause alert fatigue that erodes human oversight. We propose CoGReV, a hybrid framework that augments standard machine learning classifiers with a post-hoc non-monotonic reasoning layer implemented in Answer Set Programming. The layer applies a confidence-gated defeasible rule that revises a phishing prediction toward legitimate only when website metadata is present and the classifier's decision is low-confidence, deferring uncertain predictions to the reasoning layer while leaving out confident decisions to the classifiers. This gating acts as a function-allocation mechanism between the classifier and the reasoning layer. Unlike an ungated rule, which reduces false positives only by discarding genuine detections and degrades phishing recall by about nine percentage points, the proposed gated rule keeps recall within 0.7 percentage points of the no-revision baseline for all classifiers while revising only 0.27 percent of decisions. By lowering false alarms, without sacrificing detection, CoGReV reduces analysts alert fatigue and integrates new domain knowledge into the reasoning layer in $\mathcal{O}(n)$ time.	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	2 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.