The next generation of cellular technology, 6G, is being developed to enable a wide range of new applications and services for the Internet of Things (IoT). One of 6G's main advantages for IoT applications is its ability to support much higher data rates and bandwidth as well as to support ultra-low latency. However, with this increased connectivity will come to an increased risk of cyber threats, as attackers will be able to exploit the large network of connected devices. Generative Artificial Intelligence (AI) can be used to detect and prevent cyber attacks by continuously learning and adapting to new threats and vulnerabilities. In this paper, we discuss the use of generative AI for cyber threat-hunting (CTH) in 6G-enabled IoT networks. Then, we propose a new generative adversarial network (GAN) and Transformer-based model for CTH in 6G-enabled IoT Networks. The experimental analysis results with a new cyber security dataset demonstrate that the Transformer-based security model for CTH can detect IoT attacks with a high overall accuracy of 95%. We examine the challenges and opportunities and conclude by highlighting the potential of generative AI in enhancing the security of 6G-enabled IoT networks and call for further research to be conducted in this area.

翻译：第六代蜂窝技术6G正被开发用于支持物联网（IoT）广泛应用场景与服务。6G在物联网应用中的主要优势包括：支持更高数据速率与带宽，以及实现超低延迟。然而，这种增强的连通性将带来更高的网络威胁风险，攻击者可能利用大规模联网设备实施攻击。生成式人工智能（AI）可通过持续学习并适应新型威胁与漏洞，实现网络攻击的检测与防御。本文探讨了生成式AI在6G物联网网络威胁猎杀（CTH）中的应用，并提出一种基于生成对抗网络（GAN）与Transformer的新型CTH模型。基于新型网络安全数据集的实验分析结果表明，该Transformer架构的CTH安全模型能够以95%的整体高准确率检测物联网攻击。我们研究了相关挑战与机遇，最终强调生成式AI在增强6G物联网网络安全方面的潜力，并呼吁在该领域开展进一步研究。