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arXiv · 2412.02084

Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection

Abdullah Fajar·Setiadi Yazid·Indra Budi
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Abstract

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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Abdullah Fajar, Setiadi Yazid, Indra Budi. 2024-12-03. Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection. https://doi.org/10.35194/mji.v18i1.6501

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