对比黑盒与白盒模型在钓鱼检测中的表现，选对模型关键看是否要可解释性。
Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection
- 比较黑盒与白盒模型在钓鱼检测中的优劣，分析其适用场景。
- 白盒模型如EBM在可解释性上更优，但整体性能相近。
- 需根据是否需要可解释性来选择模型，无绝对优劣。
背景：钓鱼检测模型的可解释性有助于提升信任度并理解检测机制，从而支持攻击缓解。目标：确定并推荐能满足关键需求的方法。方法：分析黑盒与白盒模型在钓鱼检测中的优缺点，通过使用知名算法和公开钓鱼数据集进行实验验证。评估指标包括预测准确率和可解释性指标。结论：两类模型在可解释性和一致性方面表现相当，但在多样化数据集上仍有改进空间。以EBM为代表的白盒模型更适合需要可解释性及可操作洞察的应用。白盒与黑盒模型在性能与可解释性上各有优劣，应根据使用目标进行选择。
原文摘要 · Abstract (English)
Background: Explainability in phishing detection model can support a further solution of phishing attack mitigation by increasing trust and understanding how phishing can be detected. Objective: The aims of this study to determine and best recommendation to apply an approach which has several components with abilities to fulfil the critical needs Methods: A methodology starting with analyzing both black-box and white-box models to get the pros and cons specifically in phishing detection. The conclusion of the analysis will be validated by experiment using a set of well-known algorithms and public phishing datasets. Experimental metrics covers 3 measurements such as predictive accuracy and explainability metrics. Conclusion: Both models are comparable in terms of interpretability and consistency, with room for improvement in diverse datasets. EBM as an example of white-box model is generally better suited for applications requiring explainability and actionable insights. Finally, each model, white-box and black-box model has positive and negative aspects both for performance metric and for explainable metric. It is important to consider the objective of model usage.
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