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April 26, 20260 citationsOpen Access

Repository Copy of: Machine Learning Models and Explainable Artificial Intelligence Approaches for Intrusion Detection in IoT Networks

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AKAsuman Besi KütükÖÇÖzlem ÇoşkunHKHikmet Kütük

Key Points

  • The central aim is to explore the application of machine learning and explainable AI in enhancing intrusion detection systems tailored for IoT networks.
  • Details of the machine learning algorithms used for intrusion detection in IoT networks are discussed.
  • Explainable AI approaches are examined for improving the interpretability of the detection models.
  • Highlights the effectiveness of machine learning models in detecting intrusions in IoT networks.
  • Discusses the benefits of using explainable AI to provide insights into model decisions.

Abstract

This record is a repository-preserved copy of an article originally published in The European Journal of Research and Development by Orclever Science it is not the version of record, and Zenodo is not the publisher of this work. Version of Record (primary publication): https://doi.org/10.56038/ejrnd.v5i1.630 Publisher: Orclever Science & Research Group. Journal: The European Journal of Research and Development. For citation, please use the Crossref DOI and the journal citation above — not the Zenodo DOI.

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Cite This Study

Kütük et al. (2025) studied this question.

synapsesocial.com/papers/69edadd94a46254e215b5658https://doi.org/10.5281/zenodo.19745685
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