A privacy preserving intrusion detection framework for IIoT in 6G networks using homomorphic encryption and graph neural networks.

The integration of the Industrial Internet of Things (IIoT) with sixth-generation (6G) networks offers revolutionary connectivity and ultra-low latency, although it also increases cybersecurity vulnerabilities due to resource-limited devices and dynamic network structures. This research presents a novel privacy-preserving intrusion detection system (IDS) for IIoT in 6G settings, using Graph Neural Networks (GNNs) and Homomorphic Encryption (HE). The proposed system utilizes GNNs to represent intricate inter-device interactions and temporal traffic patterns, facilitating the effective identification of advanced threats, such as Advanced Persistent Threats (APTs), Distributed Denial of Service (DDoS) attacks, and Mirai botnet attacks. HE enables secure distributed training and inference by executing computations on encrypted data, thereby obviating the necessity for centralized data aggregation and assuring adherence to rigorous privacy standards like as GDPR. Experimental results indicate outstanding performance, with detection accuracies over 98%, low false positive rates, and little computing cost, rendering it appropriate for resource-constrained IIoT devices. The GNN + HE model surpasses state-of-the-art approaches in accuracy, scalability, and privacy protection, effectively tackling the dynamic difficulties of 6G-IIoT ecosystems. This study lays the groundwork for secure, scalable, and privacy-compliant IIoT systems, with future efforts directed towards adversarial robustness, real-time implementation, and integration with federated learning to improve resilience in highly interconnected industrial settings.