Threat detection in the 6G enabled Industrial IoT Networks using Deep Learning: A review on the state-of-the-art solutions, challenges and future research directions

The integration of the Industrial Internet of Things (IIoT) with sixth-generation (6G) communication technology is a critical foundation for the next generation of intelligent manufacturing and industrial automation. However, this advancement introduces significant security challenges, particularly in threat detection for IIoT systems. This paper systematically reviews existing research on threat detection in 6G-IIoT environments using Deep Learning (DL) techniques. It examines key challenges related to data processing, privacy protection, and model performance. The study first outlines the security requirements of IIoT within a 6G network environment and evaluates the application of various DL models for threat detection. It then identifies key limitations in current research, including dataset imbalance and the limited generalization capability of existing models. Finally, potential future research directions are discussed to advance the development of more intelligent and efficient threat detection mechanisms, ensuring the security and stability of IIoT systems in the 6G era.