6G-Enabled IoT Networks Cyber Threat Prevention Using Generative AI
Wael Maged Badawy · 2024
In this paper, we present the applications of Generative Artificial Intelligence (GAl) in preotecting 6G-enabled loT networks from different cyber threats. The paper shall leverage Generative Adversarial Networks (GANs) and federated learning, in order to propose a novel framework for detecting therefore mitigates sophisticated cyber-attacks in real-time. Internet of Things (loT) devices using 6G wireless networks shall provide opportunities for enhancing connectivity by enabling secured smart applications. However, these 6G advancements also introduce significant cybersecurity challenges which require robust threat prevention strategies. The proposed framework takes advantages of the inherent 6G capability, such as high data rates, low latency, and network slicing. It implemented a distributed, AI-driven security mechanism that adapts to evolving threats. The simulation results and it analysis, demonstrates the “effectiveness” of the proposed framework in preventing a wide range of cyber-attacks, such as phishing, malware, and denial-of-service (DoS) attacks. It also ensures the integrity, confidentiality, and availability of loT services. The results of this study demonstrate the integration of AI in next-generation wireless networks, and offer insights into the design of resilient and the application of adaptive cybersecurity solutions.