Revealing the Secrets of Spiking Neural Networks: The Case of Izhikevich Neuron

Luíza Caetano Garaffa, Abdullah Aljuffri, Cezar Reinbrecht, Said Hamdioui, Mottaqiallah Taouil, Johanna Sepulveda

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

7 Citations (Scopus)
212 Downloads (Pure)


Spiking Neural Networks (SNNs) are a strong candidate to be used in future machine learning applications. SNNs can obtain the same accuracy of complex deep learning networks, while only using a fraction of its power. As a result, an increase in popularity of SNNs is expected in the near future for cyber physical systems, especially in the Internet of Things (IoT) segment. However, SNNs work very different than conventional neural network architectures. Consequently, applying SNNs in the field might introduce new unexpected security vulnerabilities. This paper explores and identifies potential sources of information leakage for the Izhikevich neuron, which is a popular neuron model used in digital implementations of SNNs. Simulations and experiments on FPGA implementation of the spiking neurons show that timing and power can be used to infer important information of the internal functionality of the network. Additionally, the paper demonstrates that is feasible to perform a reverse engineering attack using both power and timing leakage.
Original languageEnglish
Title of host publication2021 24th Euromicro Conference on Digital System Design (DSD)
Subtitle of host publicationProceedings
EditorsL. O'Conner
Place of PublicationPiscataway
Number of pages5
ISBN (Electronic)978-1-6654-2703-6
ISBN (Print)978-1-6654-2704-3
Publication statusPublished - 2021
Event2021 24th Euromicro Conference on Digital System Design (DSD) - Virtual at Palermo, Spain
Duration: 1 Sept 20213 Sept 2021


Conference2021 24th Euromicro Conference on Digital System Design (DSD)
Abbreviated titleDSD 2021
CityVirtual at Palermo

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