Poster: SHAP-RM : An XAI framework for evaluating machine learning reliability

Wang, Xueyi, Li, Shancang and Liu, Yifan 2026. Poster: SHAP-RM : An XAI framework for evaluating machine learning reliability. Presented at: 22nd EAI International Conference, SecureComm 2026, Lancaster, UK, 21-24 July 2026. Published in: Cao, Yinzhi, Luo, Bo and Meng, Weizhi eds. Security and Privacy in Communication Networks. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Cham, Switzerland: Springer, pp. 499-503. 10.1007/978-3-032-32767-3_23

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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 - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	Springer
ISBN:	9783032327666
ISSN:	1867-8211
Last Modified:	04 Aug 2026 21:40
URI:	https://orca.cardiff.ac.uk/id/eprint/188535

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