Cloud-Integrated GANs: Exploring Intelligent Resource Provisioning, Anomaly Detection, and Secure Data Generation

DOI

10.2991/978-94-6463-940-7_17How to use a DOI?

Keywords

Generative Adversarial Networks (GANs); Cloud Computing; Anomaly Detection; Resource Scheduling; Cloud Security; Privacy-Preserving Machine Learning

Abstract

Generative Adversarial Networks (GANs) are now very powerful technologies in the cloud computing domain to fight major anomalies in detection, resource scheduling, cybersecurity, and privacy-preserving data generation. Recent research indicates a growing trend from simple GAN architectures toward sophisticated variants, including those that are quantum-enhanced, graph attention-based, federated GANs, and self-attention models. Improving model performance, scalability, and expressiveness is thus achieved in dynamic clouds. Integration of GANs with cloud infrastructure enables efficient training, deployment, and real-time inference using computational capability beyond that of conventional systems. Dual role of GANs in cloud security as both a threat vector and a security component has been exemplified through studies. Public-to-hybrid architecture-based cloud deployment models have also been investigated to maintain cost, privacy, and performance balance. This survey integrates recent advancements in GAN-based models in cloud and edge computing settings and charts future research areas in energy efficiency, adversarial robustness, and scalable AI-driven infrastructure.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Parthiban
AU  - Balajee Maram
PY  - 2025
DA  - 2025/12/31
TI  - Cloud-Integrated GANs: Exploring Intelligent Resource Provisioning, Anomaly Detection, and Secure Data Generation
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 231
EP  - 245
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_17
DO  - 10.2991/978-94-6463-940-7_17
ID  - Parthiban2025
ER  -