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 Article Faculty Publications -Articles 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 Dharini, N.; Janani, V.S.; Katiravan, Jeevaa Title A name given to the resource Efficient detection of intrusions in TON-IoT dataset using hybrid feature selection approach 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 Scientific Reports;Volume;16;Issue;1;Article No.;7763; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41598-026-37834-y" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-026-37834-y</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105031594104?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105031594104?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 Dharini N., Department of Computer Science and Engineering (Cyber Security), R.M.K College of Engineering and Technology, Thiruvallur, India; Janani V.S., Department of AIML and DS, Christ University, Bangalore, Kengeri Campus, Bangalore, India; Katiravan J., Department of Information Technology, Velammal Engineering College, Chennai, India Description An account of the resource This research improves IoT attack classification by introducing a bias-aware dataset refinement strategy that eliminates IP- and port-based identifiers and applies a domain-guided hybrid feature selection framework to derive a lightweight and generalizable feature set. Motivated by the need for intrusion detection models that generalize beyond predefined network configurations, this study focuses on behavior-driven network features that enable more realistic attack categorization in IoT environments. Wrapper-based feature selection methods, including forward selection, backward elimination, and genetic algorithms, identify five optimal features. To assess the robustness of the selected feature subset, both simple classifiers (Decision Tree and KNN) and ensemble learning models, including Random Forest, Gradient Boosting, XGBoost, Bagging, and Voting Ensemble, are evaluated under binary and multi-class settings. Using the proposed reduced feature set, the Decision Tree classifier achieved an accuracy of 0.986 for binary classification and 0.972 for multi-class attack classification, while the K-Nearest Neighbor classifier consistently achieved an accuracy of 0.972 for both binary and multi-class scenarios, while ensemble models yield only marginal performance improvements. Evaluation using precision, recall, F1-score, confusion matrices, and Cohens Kappa confirms that the discriminative power primarily arises from the selected feature subset rather than classifier complexity. These results demonstrate that effective feature selection enables lightweight models to achieve competitive intrusion detection performance suitable for real-world IoT deployments. The Author(s) 2026. Subject The topic of the resource Attacks; Decision tree; Hybrid feature selection; K nearest neighbours; ToN-IoT dataset Publisher An entity responsible for making the resource available Nature Research Relation A related resource ISSN: 20452322; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online