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 Mahnot, Saksham; George, Jossy P; Paul Alapatt, Bosco Title A name given to the resource An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach 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 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.1464-1471 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICV64824.2025.11085461" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV64824.2025.11085461</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013074321?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013074321?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 Mahnot S., CHRIST University, India; George J.P., CHRIST University, India; Paul Alapatt B., CHRIST University, India Description An account of the resource We perform research to develop a combined deep learning algorithm that enhances security threat detection within the Internet of Things networks. The resource variations across IoT devices create obstacles for Traditional Intrusion Detection Systems (IDSs) regarding their scalability and adaptability elements. This study explores the application of Bidirectional Recurrent Neural Networks and Long Short-Term Memory networks, which are trained on Traffic data records from NSL-KDD, a widely recognized benchmark dataset. It's a secondary dataset. This dataset is preprocessed and features are engineered to be optimized for sequential pattern recognition and handling of long-term dependency. Experimental results validate the achievement of a cross-validation accuracy of 93.40%, F1 is 91.62% and precision is 90.42%, which is greater than the individual models, such as CNN, BiRNN, or LSTM. The stacking Models Bi-RNN sequential learning and LSTM dependency retention makes the system perform better at threat classification along with elevated detection accuracy for IoT-related security issues like DoS, Probe, R2L, and U2R. The consistent performance of the model through this validation split provides evidence that the system can effectively handle IoT cybersecurity threats. 2025 IEEE. Subject The topic of the resource Bidirectional Recurrent Neural Networks; CNN; Cyber Security; Internet of Things; Long Short-Term Memory Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833151175-3; 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