Paper 2023/505 Original: eprint.iacr.org/2023/505 ↗
Side-Channel Analysis of Integrate-and-Fire Neurons within Spiking Neural Networks
Abstract
Spiking neural networks gain attention due to low power properties and event-based operation, making them suitable for usage in resource constrained embedded devices. Such edge devices allow physical access opening the door for side-channel analysis. In this work, we reverse engineer the parameters of a feed-forward spiking neural network implementation with correlation power analysis. Localized measurements of electro-magnetic emanations enable our attack, despite inherent parallelism and the resulting algorithmic noise of the network. We provide a methodology to extract valuable parameters of integrate-and-fire neurons in all layers, as well as the layer sizes.
Metadata
- Available format(s)
PDF- Category
- Attacks and cryptanalysis
- Publication info
- Preprint.
- History
- 2025-05-21: last updated
- 2023-04-07: published
- Short URL
- https://ia.cr/2023/505
- License
- https://creativecommons.org/licenses/by/4.0/
BibTeX
@misc{cryptoeprint:2023/505,
author = {Matthias Probst and Manuel Brosch and Georg Sigl},
title = {Side-Channel Analysis of Integrate-and-Fire Neurons within Spiking Neural Networks},
howpublished = {Cryptology {ePrint} Archive, Paper 2023/505},
year = {2023},
url = {https://eprint.iacr.org/2023/505}
}