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 Kumar, Prince; Sharma, Nandini; Sabarmathi, G.; Chinnaiyan, R.; Khanna, Vimal Kumar; Bushra, Seema Title A name given to the resource Fortifying Networks based AI Models for Early Vulnerability 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 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCAMS65118.2025.11234467" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCAMS65118.2025.11234467</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030063510?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030063510?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 Kumar P., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Sharma N., Lingaya's Vidyapeeth Deemed to be University, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Sabarmathi G., Christ University, School of Computer Science, Bangalore, India; Chinnaiyan R., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Khanna V.K., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Bushra S., School of Commerce &amp; Management, Lingaya's, AI/ML Lab, Centre of Excellence, Haryana, Faridabad, India Description An account of the resource Early identification of network vulnerabilities is now essential for protecting sensitive data and guaranteeing system resilience due to the increasing complexity of digital infrastructures. An extensive analysis of artificial intelligence (AI) models intended for the early identification of network vulnerabilities is presented in this research. The study examines current approaches, assesses their efficacy, and pinpoints research gaps while drawing on ideas from recent studies and cutting-edge academic research tools. The results show how AI has the ability to revolutionize cybersecurity tactics and point to new avenues for improving vulnerability detection systems. 2025 IEEE. Subject The topic of the resource AI-driven cybersecurity; AI-powered threat intelligence; anomaly detection; federated learning; graph neural networks; hybrid AI models; network vulnerability detection; quantum computing; reinforcement learning; supervised learning; unsupervised learning Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159610-1; 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