Integrating Generative AI into Network Systems: Challenges, Opportunities, and Future Directions |
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| © 2025 by IJCTT Journal | ||
| Volume-73 Issue-4 |
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| Year of Publication : 2025 | ||
| Authors : Palanichamy Manikandan | ||
| DOI : 10.14445/22312803/IJCTT-V73I4P102 | ||
How to Cite?
Palanichamy Manikandan, "Integrating Generative AI into Network Systems: Challenges, Opportunities, and Future Directions," International Journal of Computer Trends and Technology, vol. 73, no. 4, pp. 5-18, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I4P102
Abstract
This paper examines the problems and opportunities when incorporating generative AI (GAI) models in network systems. Model interpretability plays a vital role in creating trust and ensuring these technologies are used responsibly. Training such GAI models require significant computational resources, highlighting the need for advancements in both hardware and software. As AI advances, it is necessary to consider GAI usability in next-generation network technologies, including automated design, real-time optimization and AI-powered security. As a contribution, this paper reviews what happened in recent achievements of the GAI crossing to networking so far and how it can lead to more flexible and intelligent networking infrastructures. It further discusses the challenges that need to be addressed and directions for future research. Thus, the intention of this work is to analyze and explore the potential of applying GAI in next-generation network systems and ensure that it effectively meets future demands.
Keywords
Generative AI, 6G network, Network automation, Security, AI models.
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