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 Verma, Narinder; Kumar, Neerendra; Kumar, Gourav; Singh, Kuljeet Title A name given to the resource A hybrid ensemble framework with particle swarm optimization for network anomaly detection 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 Discover Applied Sciences;Volume;7;Issue;8;Article No.;921; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s42452-025-07419-x" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s42452-025-07419-x</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013160514?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013160514?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 Verma N., Department of Computer Science and Information Technology, Central University of Jammu, Jammu, 181143, India, School of Computer Science and Engineering, IILM University, Greater Noida, 201306, India; Kumar N., Department of Computer Science and Information Technology, Central University of Jammu, Jammu, 181143, India; Kumar G., Department of Computer Science and Engineering, Central University of Jammu, Jammu, 181143, India; Singh K., Department of Computer Science, School of Sciences, Christ University, Delhi-NCR, 201003, India Description An account of the resource The increasing complexity of cyber threats necessitates the development of a robust and adaptive Intrusion Detection System (IDS) capable of safeguarding network infrastructures. Traditional IDS approaches often struggle to detect sophisticated attacks due to their reliance on predefined patterns. We propose an adaptive particle swarm optimization (PSO)-optimized ensemble learning framework tailored to address these challenges in modern IDS applications. Our approach leverages the NSL-KDD and CICIDS datasets to ensure the IDS is trained and evaluated on data reflecting current network behaviours and threat landscapes. We evaluate multiple machine learning models, including Decision Trees (DT), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Random Forests (RF), and an ensemble of these models for both binary and multi-class classification tasks. By incorporating adaptive mechanisms within the PSO algorithm, our framework dynamically adjusts hyperparameters during optimization, enhancing model robustness and convergence speed. The proposed framework is also benchmarked against state-of-the-art IDS approaches, including ASRL and PSOGSA. Empirical evaluations demonstrate that the ensemble model achieves superior detection accuracy and reduced false positive rates, thereby advancing the efficacy of intrusion detection methodologies. The Author(s) 2025. Subject The topic of the resource Ensemble learning; Intrusion detection system (IDS); Network security; Particle swarm optimization (PSO) Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 30049261; 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