Hybrid recurrent with spiking neural network model for enhanced anomaly prediction in IoT networks security

These results confirm demonstrate that the proposed architecture of RNN and SSN can achieve significant advancement to IoT security. By combining both spatial and temporal feature learning, HRSNN can improve accuracy detection against diverse security threats. The model is reliable, accurate, and adaptable for safeguarding IoT networks against diverse security threats. Thus, the model addresses the potential solutions in the challenging problem of secured IoT networks.