Enhancing Industrial IoT Cybersecurity with Explainable AI: A SHAP and LIME-Based Intrusion Detection Methodology

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dc.contributor.authorAsal, Burcak
dc.contributor.authorCakin, Alperen
dc.contributor.authorDilek, Selma
dc.date.accessioned2026-02-27T07:33:13Z
dc.date.available2026-02-27T07:33:13Z
dc.date.issued2025
dc.description7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications-ICHORA
dc.description.abstractAlthough the proliferation of Industrial Internet of Things (IIoT) systems has transformed industrial operations, it has also introduced significant cybersecurity challenges. Ensuring IIoT network security requires robust, interpretable models capable of detecting and mitigating threats. This study integrates Explainable Artificial Intelligence (XAI) techniques SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) to enhance the interpretability of machine learning-based intrusion detection in IIoT. Using the WUSTL-IIoT-2021 dataset, we evaluated Conditional Variational Autoencoder (CVAE), Decision Tree (DT), and Random Forest (RF) models, analyzing their transparency and performance. SHAP and LIME identify critical features such as DstJitter, Dport, and SAppBytes, contributing to improved explainability. RF achieves near-perfect accuracy (99.99%), while optimized feature subsets maintain high accuracy with lower computational cost. The results highlight XAI's role in balancing accuracy, interpretability, and efficiency in IIoT cybersecurity, paving the way for more trustworthy intrusion detection systems.
dc.identifier.doi10.1109/ICHORA65333.2025.11017105
dc.identifier.isbn979-8-3315-1089-3; 979-8-3315-1088-6
dc.identifier.issn2996-4385
dc.identifier.urihttp://dx.doi.org/10.1109/ICHORA65333.2025.11017105
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4495
dc.identifier.wosWOS:001533792800117
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2025 7th International Congress On Human-Computer Interaction, Optimization and Robotic Applications, Ichora
dc.relation.ispartofseriesInternational Congress on Human-Computer Interaction Optimization and Robotic Applications
dc.relation.publicationcategoryKonferans ��esi - Uluslararas� - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectIIoT
dc.subjectSHAP
dc.subjectLIME
dc.subjectcybersecurity
dc.subjectintrusion detection
dc.subjectdecision tree
dc.subjectrandom forest
dc.subjectconditional variational autoencoder
dc.titleEnhancing Industrial IoT Cybersecurity with Explainable AI: A SHAP and LIME-Based Intrusion Detection Methodology
dc.typeProceedings Paper

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