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 Bellary, Keertipriya; Jalapur, Shruti; Diana Jeba Jingle, I. Title A name given to the resource Advancing Intrusion Detection Using Deep Learning: A Hybrid 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 Lecture Notes in Networks and Systems;Volume;1614 LNNS;pp.251-264 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-2878-3_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-2878-3_18</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029586132?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029586132?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 Bellary K., Department of Computer Science and Engineering, Christ University, Bangalore, India; Jalapur S., Christ University, Bangalore, India; Diana Jeba Jingle I., Christ University, Bangalore, India Description An account of the resource Intrusion detection systems (IDSs) are vital for securing networks against evolving cyberthreats. Traditional machine learning models often struggle with complex network traffic and imbalanced attack patterns. This study proposes an advanced ensemble model integrating ANN, LSTM, random forest, and LightGBM to enhance detection accuracy and robustness. Evaluations on the KDD99 dataset demonstrate that the ensemble model outperforms standalone ANN-LSTM models, achieving 92.4% accuracy, 97.4% precision, 87.1% recall, and a 91.9% F1 score. Hybrid models also showed significant improvements, with Nadam optimization yielding an F1 score of 93.10% for ANN-LSTM-random forest and Adam optimization achieving 93.30% for ANN-LSTM-LightGBM. By addressing data imbalance and improving attack pattern detection, this approach provides a scalable, efficient solution for real-time intrusion detection with superior performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Accuracy; Artificial neural networks (ANNs); Cybersecurity; Ensemble model; F₁ score; Hybrid models; Intrusion detection system (IDS); KDD99 dataset; LightGBM; Long short-term memory (LSTM); Nadam); Optimizers (Adam; Precision; Random forest; Real-time detection; Recall Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981952877-6; 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