PHISHREV framework combines ML with reasoning for better phishing detection

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Researchers have developed PHISHREV, a novel framework that combines machine learning with non-monotonic reasoning to enhance phishing website detection. This hybrid approach uses Answer Set Programming to refine machine learning predictions by incorporating expert knowledge, leading to improved decision consistency. A significant advantage is the ability to integrate new domain knowledge efficiently without retraining the entire model. AI

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IMPACT Introduces a hybrid reasoning approach to improve AI-based security systems, potentially reducing false positives and enabling faster knowledge updates.

RANK_REASON Academic paper introducing a new framework for phishing detection.

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arXiv cs.AI TIER_1 · Amlan Chakrabarti · 2026-04-28 11:20

PHISHREV: A Hybrid Machine Learning and Post-Hoc Non-monotonic Reasoning Framework for Context-Aware Phishing Website Classification

Phishing detection systems are predominantly rely on statistical machine learning models, which often lack contextual reasoning and are vulnerable to adversarial manipulation. In this work, we propose a hybrid framework that integrates machine learning classifiers with non-monoto…

COVERAGE [1]

PHISHREV: A Hybrid Machine Learning and Post-Hoc Non-monotonic Reasoning Framework for Context-Aware Phishing Website Classification

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