Experimental Investigation of Side-Channel Attacks on Neuromorphic Spiking Neural Networks

Abstract

This study investigates the reliability of commonly utilized digital spiking neurons and the potential side-channel vulnerabilities in neuromorphic systems that employ them. Through our experiments, we have successfully decoded the parametric information of Izhikevich and leaky integrate-and-fire (LIF) neuron-based spiking neural networks (SNNs) using differential power analysis. Furthermore, we have demonstrated the practical application of extracted information from the 92% accurate pretrained standard spiking convolution neural network classifier on the FashionMNIST dataset. These findings highlight the potential dangers of utilizing internal information for side-channel and denial-of-service attacks, even when using the usual input as the attack vector.