用逻辑推理提升钓鱼网站识别的上下文理解能力
PHISHREV: A Hybrid Machine Learning and Post-Hoc Non-monotonic Reasoning Framework for Context-Aware Phishing Website Classification
- 结合机器学习与非单调逻辑推理，实现决策修正
- 推理模块修改5.08%的分类结果，提升判断一致性
- 新增知识可快速融入，无需重新训练模型
钓鱼网站检测系统主要依赖统计机器学习模型，常缺乏上下文推理能力且易受对抗攻击。本文提出一种混合框架，将机器学习分类器与基于答案集编程（ASP）的非单调推理相结合，实现上下文感知的决策优化。后处理推理层通过形式化信念修正机制，引入专家知识来调整分类器输出。实验表明，该推理模块修改了5.08%的原始分类结果，显著提升了决策一致性。其核心优势在于新领域知识可按$/mathcal{O}(n)$时间复杂度快速注入推理层，无需重新训练模型。
原文摘要 · Abstract (English)
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.
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