Using XAI Techniques to Detect Targeted Data Poisoning Attacks on Healthcare Applications of Machine Learning Systems

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dc.contributor.authorEyad Dhaher Megdadi
dc.contributor.authorUsman Javed Butt
dc.date.accessioned2025-05-27T11:57:34Z
dc.date.available2025-05-27T11:57:34Z
dc.date.issued2025
dc.description.abstractThis research study explores the application of Explainable Artificial Intelligence (XAI) methods for detecting targeted data poisoning attacks in healthcare machine learning systems. As machine learning becomes increasingly integrated into critical fields like healthcare, the integrity and security of training data have become paramount concerns. Data poisoning attacks, which manipulate training datasets to influence model behaviour, pose a significant threat to the reliability and effectiveness of these systems. Our study presents a novel approach that leverages XAI techniques, particularly focusing on global explanations of selected features, to identify signs of data manipulation. We propose a method of monitoring the impact level of carefully chosen features as an indicator of potential poisoning, using predetermined thresholds to trigger warnings when unusual patterns are detected. The research methodology involves applying global explanation method, to measure and monitor features impact in healthcare datasets, then explore the effectiveness of this approach using a case study on hypothyroid diagnosis, where data poisoning could lead to delayed treatment with potentially life-threatening consequences. Research findings suggest that XAI techniques can provide valuable insights into the behaviour of machine learning models, enabling more effective detection of subtle, targeted poisoning attacks. However, we also acknowledge limitations, including the need for some prior knowledge of potential attack goals and the risk of false positives or negatives.
dc.identifier.citationMegdadi, E.D., Butt, U.J. (2025). Using XAI Techniques to Detect Targeted Data Poisoning Attacks on Healthcare Applications of Machine Learning Systems. In: Al Marri, K., Mir, F.A., Awad, A., Abubakar, A. (eds) BUiD Doctoral Research Conference 2024. BDRC 2024. Lecture Notes in Civil Engineering, vol 587. Springer, Cham. https://doi.org/10.1007/978-3-031-84371-6_40
dc.identifier.isbnHB: 9783031843709 eBook: 9783031843716
dc.identifier.urihttps://bspace.buid.ac.ae/handle/1234/3130
dc.language.isoen
dc.publisherSpringer Cham
dc.relation.ispartofseriesLecture Notes in Civil Engineering; 587
dc.titleUsing XAI Techniques to Detect Targeted Data Poisoning Attacks on Healthcare Applications of Machine Learning Systems
dc.typeBook chapter
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