Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Paul, Aditi; Kumari, Sweety; Navadia, Nipun R; Sinha, Somnath Title A name given to the resource Explainable Intrusion Detection System for Internet of Things-explainability with reliability Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of 5th International Conference on Soft Computing for Security Applications, ICSCSA 2025;pp.226-232 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSCSA66339.2025.11170796" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSCSA66339.2025.11170796</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018461660?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018461660?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Paul A., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Kumari S., Banasthali Vidhapith, Department of Computer Science, Jaipur, India; Navadia N.R., Pace University, New York City, NY, United States; Sinha S., Christ University, Department of Computer Science, Bengaluru, India Description An account of the resource Explainable Artificial Intelligence (XAI) based Intrusion Detection System (IDS) (X-IDS) has transformed the traditional IDS into interpretable and transparent system with the goal of providing interpretable justification for IDS models. XAI is now being used to extract more appropriate features for specific cyber-attacks. The black-box model of ML based IDS is not capable of giving reason for false positive to the cyber defense personnel. XAI tools reduces this abstraction by locally interpreting the model's behaviour at some datapoints along with global interpretability. This article proposes an explainable IDS by using XAI tools. We used SHAP (SHapley Additive exPlanations) to identify the variations in feature importance of selected ML based IDSs and explain the variations of their detection accuracies. Also, we have shown that with same dataset, feature importance varies differently with different ML models. This leads us to the conclusion that specific set of features are required for specific ML models while other can be discarded. The explainability proposed in this study also help to select less set of features to overcome time of execution and cost. 2025 IEEE. Subject The topic of the resource DoS attack; feature importance; Intrusion Detection System; XAI based IDS; XAI for machine learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159491-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online