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 Swain, Anugraha; Poonia, Ramesh Chandra; Shanbhog, Manjula Title A name given to the resource A Lightweight Hybrid Deep Learning model for Real-Time Intrusion Detection in IoT Networks. 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 Proceedings of International Conference on Digital Innovations for Sustainable Solutions, ICDISS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDISS68238.2025.11320624" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDISS68238.2025.11320624</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105033630956?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105033630956?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 Swain A., Department of Computer Science, CHRIST University, Bangalore, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, India; Shanbhog M., Department of Computer Science, CHRIST University, Bangalore, India Description An account of the resource In IoT networks, cyber threats are hard to identify because of dynamic and heterogenic nature of IoT traffic as it cannot be identified with more traditional intrusion detection systems. This paper discusses the deep learning methods of intrusion detection in terms of CNN+LSTM, CNN+BiLSTM, CNN+GRU, and BiGRU+RF and propose a novel Dense+SimpleRNN architecture. Preprocessing includes label encoding, feature selection, normalization, SMOTE balancing, and reshaping sequences, using the RT-IoT 2022 dataset. The paper demonstrates, CNN + BiLSTM and CNN + GRU achieving similar accuracy but with higher computational cost. On the other hand, the proposed Dense+SimpleRNN has 98.59% accuracy, precision and recall and Fl-score, which are higher than the baselines models. The results point that Dense+SimpleRNN is an efficient and lightweight IDS that is very appropriate in real-time IoT network security. 2025 IEEE. Subject The topic of the resource Deep Learning; Dense-SimpleRNN; Internet of Things; Intrusion Detection System; ThreatAssessment Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155641-9; 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