A neural-symbolic dynamic graph framework for real-time anomaly detection in software-defined industrial cyber-physical systems
NS-MFM-DGA is introduced, a neural-symbolic multimodal foundation model that incorporates dynamic graph attention for real-time anomaly detection in SD-ICPS and provides a feasible and explainable low-latency anomaly-detection framework for supervisory monitoring in SD-ICPS.
Sen-Lin Jiang, Wen-Jian Zhang, Hao Pan
· Engineering Research Express · 0 citations