@article{al-hawawreh_2023, 
  title={Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks}, 
  abstractNote={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.}, 
  author={Al-Hawawreh}, 
  year={2023}, 
  month={Mar}
  }