Hybrid GNN-LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems.

Neuromorphic computing, which is based on spiking neural networks (SNNs) and event cameras, can provide energy-efficient autonomous vehicle (AV) perception, yet is exceedingly susceptible to adversarial perturbations, fault injections, and data poisoning. Conventional defences may prove inadequate on the spot scenarios with a small amount of edge resources. The systemic security solution proposed in the paper consists of a Hybrid Graph Neural Network-Long Short-Term Memory (GNN-LSTM) attack detection model with a Differential Privacy (DP) and Secure Multi-Party Computation (SMPC) solution to privacy and threat-reduction respectively. Quantization and pruning are also used to optimise the framework to support edge deployment. KITTI multimodal experiment results indicate 94.3 percent accuracy and lower the attack success rate by 30 percent. The neuromorphic N-Caltech101 experiments reach an accuracy of 92.4 percent with a drop of 27 percent. These results confirm that the proposed solution can offer substantial, privacy-conscious and resource-efficient security of next-generation neuromorphic autonomous systems against trained adversarial attacks.