Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909413137186816 |
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| author | Fajar, Abdullah Yazid, Setiadi Budi, Indra |
| author_facet | Fajar, Abdullah Yazid, Setiadi Budi, Indra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02084 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection Fajar, Abdullah Yazid, Setiadi Budi, Indra Cryptography and Security Artificial Intelligence 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. |
| title | Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2412.02084 |