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 Joseph, Eldo Ephraim; Sharma, Vandana; Shoran, Preety Title A name given to the resource A Comparative Study on IOT Security Using Machine Learning Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1496 LNNS;pp.439-450 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8107-5_32" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8107-5_32</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028314790?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028314790?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 Joseph E.E., Christ University, Bengaluru, India; Sharma V., Christ University, Bengaluru, India; Shoran P., Christ University, Bengaluru, India Description An account of the resource This increased reliance on networks has made the security of IoT devices a highly burning issue. Among the sea of threats, the threats associated with DDoS attacks are at a high level since they do damage to the functionality of devices and unavailability of networks. Detection and mitigation of DDoS attacks will demand IoT environments based on powerful classification algorithms. This paper evaluates the performances of three prominent algorithms: Decision Tree, Random Forest, and Histogram-Based Gradient Boosting for the classification of DDoS attack traffic within IoT networks. An IoT-23 dataset comprising a subset of attacks, including DDoS, is used herein for the purpose of achieving high classification accuracy to ensure a reliable evaluation of attacks. The results clearly show that all three algorithms are pretty good in terms of detection performance, and Histogram-Based Gradient Boosting is the best in terms of generalization accuracy. These results open new perspectives for the implementation of machine learning, generally, and Histogram-Based Gradient Boosting, specifically, directed to improving security in IoT networks against DDoS attacks, which is an extremely promising result when working within the light of some insights for future research and development within this critical area of security. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence; Cybersecurity; Internet of Things (IoT); Machine learning Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968106-8; 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