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 Book Chapter Faculty Publications- Book Chapter 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 Kanna, R. Rajesh; Priya, T. Mohana; Onyema, Edeh Michael; Goel, Shreya; Varghese, Jithu Title A name given to the resource Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks 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 Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.196-205 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003652991-14" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003652991-14</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040282191?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040282191?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 Kanna R.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Priya T.M., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Onyema E.M., Department of Mathematics and Computer Science, Coal City University, Enugu, Nigeria; Goel S., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Varghese J., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India Description An account of the resource Cyberthreat Intelligence (CTI) feeds serve as crucial resources for organizations seeking to fortify their defenses against emerging cyberthreats. However, these feeds often suffer from deficiencies such as incomplete data, false positives, and a lack of contextual information. This chapter proposes an innovative approach to address these challenges by leveraging Generative Adversarial Networks (GANs) to enhance CTI feeds. We introduce ThreatGAN, a novel GAN architecture specifically designed for cyberthreat modeling. Trained on accurate CTI data, ThreatGAN learns to generate synthetic yet realistic threat indicators, including malicious uniform resource locators (URLs), Internet Protocol (IP) addresses, and attack patterns. We demonstrate the efficacy of ThreatGAN in filling gaps in existing feeds, reducing false positives, and providing essential contextual information. The quantitative and qualitative evaluation shows that ThreatGAN significantly improves CTI quality. This technique can strengthen organizations cyber defenses by enabling them to work with higher quality, more complete Threat Intelligence. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-100365299-1; 978-104109801-0; 978-104110023-2; Language A language of the resource English Type The nature or genre of the resource Book chapter 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