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 Analyzing the Role of LIME and SHAP in Explainable DoS Attack Detection for IoT Systems 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 4th International Conference on Automation, Computing and Renewable Systems, ICACRS 2025 - Proceedings;pp.428-434 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICACRS67045.2025.11324170" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICACRS67045.2025.11324170</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032756418?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032756418?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 tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) are extensively used in various detection and prediction approaches. These tools extract feature importance from the datasets and explain the contribution of the features (feature importance) towards detection /prediction output both locally and globally. In the current study a performance analysis is represented on the behaviour of LIME and SHAP explainability towards Denial-of-Service Attack detection in Internet of Things. There are numerous Black-box models including Machine Learning which show high detection accuracies in such case but the output is not interpretable by the security analyst most of the time. this drawback is overcome by introducing LIME and SHAP interpretability to the output of BlackBox model by analysing feature importance of the attack dataset towards detection accuracy. However, LIME and SHAPE has different behaviour towards model-interpretability. SHAP is powerful in global explanation where LIME works efficiently on local interpretation. We have shown that these two different tools perform on same detection accuracies of DoS attack using Machine learning model. A random forest classifier is first selected with high detection accuracy on a simulated DoS attack dataset and at the output SHAP and LIME are executed for achieving both local and global explainability. The comparison shows how SHAP and LIME show strength and weakness in explaining model's behaviour both locally and globally. 2025 IEEE. Subject The topic of the resource DoS attack; feature importance; LIME and SHAP comparison; XAI based tools; 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: 979-833154886-5; 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