Side-Channel Analysis of Integrate-and-Fire Neurons Within Spiking Neural Networks

Abstract

Spiking neural networks gain increasing attention in constraint edge devices due to event-based low-power operation and little resource usage. Such edge devices often allow physical access, opening the door for Side-Channel Analysis. In this work, we introduce a novel robust attack strategy on the neuron level to retrieve the trained parameters of an implemented spiking neural network. Utilizing horizontal correlation power analysis, we demonstrate how to recover the weights and thresholds of a feed-forward spiking neural network implementation. We verify our methodology with real-world measurements of localized electromagnetic emanations of an FPGA design. Additionally, we propose countermeasures against the introduced novel attack approach. We evaluate shuffling and masking as countermeasures to protect the implementation against our proposed attack and demonstrate their effectiveness and limitations.