A Generative AI-Enhanced Adaptive Cybersecurity Framework for 6G Networks: Comparative Evaluation and Results

Omar Hisham Rasheed Alsadoon

Abstract: In advanced network data processing, security will rely on generative artificial intelligence. The advent of 6G networks poses an elevated risk of widespread cyberattacks due to reduced latency and expanded connectivity. That is, if the force behind an epochal era of technological change is a generative AI-based defensive platform able to pick up threats in their emergent phases-with both GAN simulation and intelligent language probing for signs of danger, as well Digital twin enforcement, which adheres to modeled protocols while adapting to dynamic threat patterns for differentiating degrees of fit--there's no way traditional security systems can compare with this let alone be replaced altogether. This study introduces a Generative AI-Enhanced Adaptive Cybersecurity Platform (GAI-ACP) tailored for 6G networks. The proposed framework integrates Generative Adversarial Networks (GANs), Large Language Models (LLMs), and Digital Twin (DT) technologies to proactively detect and mitigate advanced cyber threats. Through comparative analysis against baseline models, GAI-ACP demonstrated superior detection accuracy (up to 99.4%), reduced latency (by 34.2%), and enhanced adaptability. The system's closed-loop architecture enables real-time simulation, reasoning, and response, making it a viable solution for next-generation network security demands

Keywords: Generative AI, Adaptive Cybersecurity, 6G Networks, Threat Detection, Digital Twin Simulation

How to Cite?: Omar Hisham Rasheed Alsadoon, "A Generative AI-Enhanced Adaptive Cybersecurity Framework for 6G Networks: Comparative Evaluation and Results", Volume 14 Issue 11, November 2025, International Journal of Science and Research (IJSR), Pages: 885-891, https://www.ijsr.net/getabstract.php?paperid=SR251105191859, DOI: https://dx.doi.org/10.21275/SR251105191859