ItemBook Chapter

Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks

Title
Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks
Creator
Kanna, R. Rajesh; Priya, T. Mohana; Onyema, Edeh Michael; Goel, Shreya; Varghese, Jithu
Description
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.
Source
Generative Adversarial Networks for Cybersecurity:: Protecting Data and Networks;pp.196-205
Date
01-01-2026
Publisher
CRC Press
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-100365299-1; 978-104109801-0; 978-104110023-2;
Format
online
Language
English
Type
Book chapter

Citation

Kanna, R. Rajesh; Priya, T. Mohana; Onyema, Edeh Michael; Goel, Shreya; Varghese, Jithu, “Enhancing Cybethreat Intelligence Feeds Using Generative Adversarial Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24454.

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