Deep Learning Based Cyber Attack Detection in 6G Wireless Networks

This paper presents a novel deep learning-based approach to detect various cyber attacks within 6G wireless networks, encompassing DoS, probe attacks, and Sybil attacks. Leveraging the KDD Cup dataset and implementing our solution using PyTorch, our method demonstrates remarkable effectiveness, surpassing conventional techniques. Our results showcase the model's adaptability to evolving attack patterns, underscoring its potential in bolstering the security of 6G wireless networks. This research significantly contributes to the field of intrusion detection in the 6G wireless networks landscape, offering insights into the application of deep learning to tackle emerging cyber threats. With the continuous advancement of 6G networks, our proposed approach stands as a pivotal means of safeguarding network integrity and availability against a spectrum of cyber attacks. This study not only furthers intrusion detection in 6G wireless networks but also highlights the pivotal role of deep learning in addressing the dynamic and evolving nature of cyber threats.