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

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 52 min agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 52 min agohigh
arXiv id: 2604.25512
Source:arXiv (Atom API + RSS)T1observed 52 min agohigh
Categories: cs.AI
Source:arXiv (Atom API + RSS)T1observed 52 min agohigh
PDF: arxiv.org/pdf/2604.25512
Source:arXiv (Atom API + RSS)T1observed 52 min agohigh
Primary category: cs.AI
Source:arXiv (Atom API + RSS)T1observed 52 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 52 min agohigh

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T19

Freshest observation

52 min ago

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Authors: Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti

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Claim history

9 claims · 9 properties

Official pageofficial_url1

Claim history for Official page
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/abs/2604.25512	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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

Arxiv announce typearxiv_announce_type1

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replace	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

arXiv idarxiv_id1

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Value	Valid from → to	Status	Source	Confidence	Extractor
2604.25512	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Authorsauthors1

Claim history for Authors
Value	Valid from → to	Status	Source	Confidence	Extractor
Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Categoriescategories1

Claim history for Categories
Value	Valid from → to	Status	Source	Confidence	Extractor
cs.AI	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

PDFpdf_url1

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https://arxiv.org/pdf/2604.25512	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Primary categoryprimary_category1

Claim history for Primary category
Value	Valid from → to	Status	Source	Confidence	Extractor
cs.AI	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Publishedpublished_at1

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Value	Valid from → to	Status	Source	Confidence	Extractor
11 Sept 2026	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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11 Sept 2026
New paperPaperCoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification
New paper: CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	52 min ago	1

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