Inter-Architecture Portability of Artificial Neural Networks and Side Channel Attacks

Manoj Gopale, Gregory Ditzler, Roman Lysecky, Janet Roveda

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Side-channel attacks (SCA) have been studied for several decades, which resulted in many techniques that use statistical models to extract system information from side channels. More recently, machine learning has shown significant promise to advance the ability for SCAs to expose vulnerabilities. Artificial neural networks (ANN) can effectively learn nonlinear relationships between features within a side channel. In this paper, we propose a multi-architecture data aggregation technique to profile power traces for a system with an embedded processor that is based on three types of deep NNs, namely, multi-layer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN). This is one of the first works to explore the inter-architecture portability of NNs and SCAs. We demonstrate the robustness of the ANNs performing power-based SCAs on multiple architecture configurations with different architectural features, such as L1/L2 caches' size and associativity, and system memory size.

Original languageEnglish (US)
Title of host publicationGLSVLSI 2022 - Proceedings of the Great Lakes Symposium on VLSI 2022
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450393225
StatePublished - Jun 6 2022
Event32nd Great Lakes Symposium on VLSI, GLSVLSI 2022 - Irvine, United States
Duration: Jun 6 2022Jun 8 2022

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI


Conference32nd Great Lakes Symposium on VLSI, GLSVLSI 2022
Country/TerritoryUnited States


  • portability embedded security
  • side channel attack

ASJC Scopus subject areas

  • General Engineering

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