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

2026-04-28

AI Summary

This work addresses the limitations of traditional phishing website detection methods, which rely on statistical machine learning models that lack contextual reasoning capabilities and are vulnerable to adversarial attacks. The authors propose a non-monotonic reasoning framework that integrates a machine learning classifier with Answer Set Programming (ASP), augmented by a belief revision layer that dynamically incorporates expert knowledge for context-aware decision refinement. This post-prediction module enables seamless integration of new domain knowledge without retraining the underlying model, operating with O(n) time complexity. Experimental results demonstrate that the reasoning component successfully corrects 5.08% of the classifier’s initial predictions, significantly enhancing the consistency and robustness of the detection outcomes.

Abstract

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-monotonic reasoning using Answer Set Programming (ASP) to enable context-aware decision refinement. The proposed post-hoc reasoning layer incorporates expert knowledge to revise classifier predictions through formal belief revisions. Experimental results indicate that the reasoning module modifies 5.08\% of classifier outputs, leading to improved decision consistency. A key advantage is that new domain knowledge can be incorporated into the reasoning layer in $\mathcal{O}(n)$ time, eliminating the need for model retraining.

Problem

• phishing detection
• context-aware classification
• adversarial manipulation
• machine learning limitations
• contextual reasoning

Innovation

• hybrid reasoning
• non-monotonic reasoning
• Answer Set Programming
• context-aware classification
• post-hoc refinement