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PHISHREV framework combines ML with reasoning for better phishing detection

By PulseAugur Editorial · [1 sources] ·

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

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.

Read on arXiv cs.AI →

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

PHISHREV framework combines ML with reasoning for better phishing detection

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Amlan Chakrabarti ·

    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…

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