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 Mahapatra, Abhipsa; Alapatt, Bosco Paul; George, Jossy P Title A name given to the resource Deep Learning-based Cybersecurity Framework for IoT Environments 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.1472-1477 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICICV64824.2025.11085802" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICICV64824.2025.11085802</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013071188?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013071188?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 Mahapatra A., Christ University, India; Alapatt B.P., Christ University, India; George J.P., Christ University, India Description An account of the resource The article "Cyber Security In IOT using deep learning Approach"presumably states the pressing need for increased cybersecurity within the rapidly growing Internet of Things (IoT) paradigm. It stresses the distinct problems brought by IoT settings, such as distributed systems and heterogeneous devices, that make the old methods of security inoperable. The article we underscores the promise of deep learning and AI as novel solutions for identifying and foiling cyberattacks, but also notes the emergence of adversarial AI employed by cybercriminals. In addition, it urges proactive cybersecurity measures and ongoing surveillance to counter changing threats, especially with IoT networks becoming increasingly complex with applications in smart cities and other industries. The abstract can conclude by emphasizing the need for continued research into AI-based cybersecurity solutions to guarantee effective protection against emerging threats. 2025 IEEE. Subject The topic of the resource Cybersecurity; Deep Learning; Internet Of Things; Intrusion Detection Systems; Machine Learning Algorithms 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