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 Vineetha, K.R.; Resmi, K.R.; Amrutha, K.; Blessie, E. Chandra; Omanakuttan, Midhun Title A name given to the resource GAN-Based Metaheuristic Techniques for Data Generation and Imbalance Data Control 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.220-234 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003652991-16" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003652991-16</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040279942?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040279942?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 Vineetha K.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Resmi K.R., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Amrutha K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, Karnataka, India; Blessie E.C., School of Innovation, KG College of Arts and Science, Tamil Nadu, Coimbatore, India; Omanakuttan M., Computing Department, International College of Dundee Scotland, United Kingdom Description An account of the resource In cybersecurity today, the power of features such as threat detection, anomaly spotting, and predictive analytics depends heavily on having abundant, properly dispersed datasets. The actual datasets often fall short, suffering both from a lack of volume and skewed class distribution, for example, a flood of routine network activity records overshadowing the infrequent but vital records of malicious behavior. The performance of data-driven models hinges on access to abundant, well-distributed data. However, real-world datasets frequently exhibit inadequate sample sizes and pronounced class imbalances, limiting the viability of complex models. This chapter proposes a novel strategy for generating synthetic data and effectively managing class imbalance, leveraging the integration of Generative Oppositional Networks (GANs) and sophisticated metaheuristic optimization techniques. Rather than settling for fixed GAN architectures, our approach progressively enhances a dynamic GAN framework by deploying a metaheuristic search to identify optimal network topologies, antidote scaling factors, and training schedules. This iterative calibration enables the model to adaptively respond to the imbalance and ensures a richer, balanced synthetic training environment. This adaptive optimization addresses common GAN training pitfalls, mode collapse, and instability while consistently producing synthetic samples that are both precise and varied. What sets the framework apart is its built-in ability to detect and over-represent minority classes, intelligently augmenting the dataset to correct class imbalance without falling back on naive duplication. By equipping GANs with metaheuristic reasoning, the study seeks to elevate data synthesis beyond current limits, generating more robust and impartial machine learning models in any domain where data collection is limited or systematically skewed. 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