XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection
Spam detection is a critical cybersecurity and information management task with significant implications for security decision-making processes. Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media Deutschland GmbH
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118711/ http://psasir.upm.edu.my/id/eprint/118711/1/118711.pdf |
| _version_ | 1848867579666366464 |
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| author | Bouke, Mohamed Aly Alramli, Omar Imhemed Abdullah, Azizol |
| author_facet | Bouke, Mohamed Aly Alramli, Omar Imhemed Abdullah, Azizol |
| author_sort | Bouke, Mohamed Aly |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Spam detection is a critical cybersecurity and information management task with significant implications for security decision-making processes. Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM) have been employed to mitigate this challenge. However, these algorithms often suffer from the "black box" dilemma, a lack of transparency that hinders their applicability in security contexts where understanding the reasoning behind classifications is essential for effective risk assessment and mitigation strategies. To address this limitation, the current paper leverages Explainable Artificial Intelligence (XAI) principles to introduce a novel, more transparent approach to spam detection. This paper presents a novel approach to spam detection using a Random Forest (RF) Classifier model enhanced by a meticulously designed methodology. The methodology incorporates data balancing through Hybrid Random Sampling, feature selection using the Gini Index, and a two-layer model explainability via Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) techniques. The model achieved an impressive accuracy rate of 94.8% and high precision and recall scores, outperforming traditional methods such as LR, KNN, DT, and SVM across all key performance metrics. The results affirm the effectiveness of the proposed methodology, offering a robust and interpretable model for spam detection. This study is a significant advancement in the field, providing a comprehensive and reliable solution to the spam detection problem. |
| first_indexed | 2025-11-15T14:38:45Z |
| format | Article |
| id | upm-118711 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:38:45Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1187112025-07-22T07:20:53Z http://psasir.upm.edu.my/id/eprint/118711/ XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection Bouke, Mohamed Aly Alramli, Omar Imhemed Abdullah, Azizol Spam detection is a critical cybersecurity and information management task with significant implications for security decision-making processes. Traditional machine learning algorithms such as Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT), and Support Vector Machines (SVM) have been employed to mitigate this challenge. However, these algorithms often suffer from the "black box" dilemma, a lack of transparency that hinders their applicability in security contexts where understanding the reasoning behind classifications is essential for effective risk assessment and mitigation strategies. To address this limitation, the current paper leverages Explainable Artificial Intelligence (XAI) principles to introduce a novel, more transparent approach to spam detection. This paper presents a novel approach to spam detection using a Random Forest (RF) Classifier model enhanced by a meticulously designed methodology. The methodology incorporates data balancing through Hybrid Random Sampling, feature selection using the Gini Index, and a two-layer model explainability via Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP) techniques. The model achieved an impressive accuracy rate of 94.8% and high precision and recall scores, outperforming traditional methods such as LR, KNN, DT, and SVM across all key performance metrics. The results affirm the effectiveness of the proposed methodology, offering a robust and interpretable model for spam detection. This study is a significant advancement in the field, providing a comprehensive and reliable solution to the spam detection problem. Springer Science and Business Media Deutschland GmbH 2024-10-30 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/118711/1/118711.pdf Bouke, Mohamed Aly and Alramli, Omar Imhemed and Abdullah, Azizol (2024) XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection. International Journal of Information Security, 24. art. no. 5. pp. 1-19. ISSN 1615-5262; eISSN: 1615-5270 https://link.springer.com/article/10.1007/s10207-024-00920-1?error=cookies_not_supported&code=b75fe216-8cf5-4c7a-a002-3c363577d6ca 10.1007/s10207-024-00920-1 |
| spellingShingle | Bouke, Mohamed Aly Alramli, Omar Imhemed Abdullah, Azizol XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection |
| title | XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection |
| title_full | XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection |
| title_fullStr | XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection |
| title_full_unstemmed | XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection |
| title_short | XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection |
| title_sort | xairf-wfp: a novel xai-based random forest classifier for advanced email spam detection |
| url | http://psasir.upm.edu.my/id/eprint/118711/ http://psasir.upm.edu.my/id/eprint/118711/ http://psasir.upm.edu.my/id/eprint/118711/ http://psasir.upm.edu.my/id/eprint/118711/1/118711.pdf |