Federated Digital Twin With Spatiotemporal Learning and Postquantum Security for Robust IIoT Anomaly Detection

The industrial Internet of Things (IIoT) is increasingly vulnerable to sophisticated cyber-attacks, including zero-day and device-specific threats, which pose severe operational and security risks. We present a federated digital twin (DT) for a spatiotemporal anomaly detection approach that is tailored to offer a real-time, scalable, and privacy-preserving security for IIoT settings. The framework integrates DT modeling to represent the physical network, spatiotemporal deep learning (DL) for capturing complex dependencies across devices and time, federated learning (FL) to enable collaborative model training without exposing sensitive data, and postquantum cryptographic (PQC) security to ensure robust protection against emerging quantum-era threats. We evaluate the framework on the RT-IoT2022 dataset, demonstrating an overall detection accuracy of 99.89%, precision of 99.91%, recall of 99.87%, and an area under the ROC curve (AUC) of 0.998. The proposed method surpasses contemporary intrusion detection systems (IDSs), accurately detects zero-day attacks, and reduces the overhead of communication by 65% in comparison with centralized training. Ablation studies demonstrate the importance of multiview graph learning, cross-graph attention, and federated aggregation for obtaining good performance. In addition, the framework offers transparent descriptions of the anomalies, so that operators can name and take action against malicious operations. Its federated architecture ensures sensitive IIoT data remains on local devices, enhancing privacy and compliance with data protection regulations. The integration of PQC mechanisms future-proofs the system against quantum-era attacks, making it resilient to next-generation threats. Overall, the proposed framework offers a holistic, robust, and scalable solution for securing industrial networks and supports deployment across diverse IIoT infrastructures with minimal operational overhead.