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New framework CoGReV enhances phishing detection with AI reasoning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CoGReV, a new framework designed to improve phishing website detection by integrating machine learning with non-monotonic reasoning. This system uses a confidence-gated rule to revise predictions, specifically downgrading uncertain classifications to 'legitimate' only when website metadata is available. This approach aims to reduce false positives and analyst alert fatigue without significantly impacting the detection rate of actual phishing sites. AI

IMPACT This framework could reduce alert fatigue for human analysts by improving the accuracy of phishing detection systems.

RANK_REASON The cluster describes a new research paper detailing a novel framework for AI-based phishing detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework CoGReV enhances phishing detection with AI reasoning

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The cluster describes a new research paper detailing a novel framework for AI-based phishing detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mainak Sen, Kumar Sankar Ray, Amlan Chakrabarti ·

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

    arXiv:2604.25512v3 Announce Type: replace 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. …

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