Wang, Xueyi, Li, Shancang and Burnap, Peter ![]() Item availability restricted. |
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Abstract
This research presents a novel framework, SHAP-RM, for assessing the reliability and trustworthiness of machine learning (ML) models in cyber security applications using explainable artificial intelligence (XAI). By applying SHAP (SHapley Additive exPlanations) to XGBoost regression and classification tasks trained on the live PV generation dataset and the UNSW-NB15 dataset, we examine the interpretability and robustness of model decisions. Integrating XAI enhances transparency, offering actionable insights for refining and securing ML-driven defense systems.
Item Type: | Conference or Workshop Item (Poster) |
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Status: | In Press |
Schools: | Schools > Computer Science & Informatics |
Date of First Compliant Deposit: | 9 August 2025 |
Date of Acceptance: | 29 July 2025 |
Last Modified: | 12 Aug 2025 10:46 |
URI: | https://orca.cardiff.ac.uk/id/eprint/180317 |
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