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Glimpsing the Leakage Flow for Deep Learning-Based Side-Channel Analysis using Stochastic Leakage Model
eprint.iacr.orgpaper

Glimpsing the Leakage Flow for Deep Learning-Based Side-Channel Analysis using Stochastic Leakage Model

GlimpseDNN is a model-agnostic framework that uses a stochastic leakage model to visualize, layer-by-layer, what physical leakage information a deep neural network extracts during profiled side-channel analysis (SCA) to recover secret keys. The authors (Trevor Yap and Shivam Bhasin, NTU) validate it on simulated traces and public datasets (ASCADf, ASCADr, ASCADf_desync50, ASCADf_desync100), showing it can reveal leakages even in deeper network layers when they are not visible in raw traces, with applications to designing more side-channel-resistant hardware.

TL;DR A new explainability framework (GlimpseDNN) uses a stochastic leakage model to show which layers of a DNN extract secret-key leakage during profiled side-channel attacks on ASCAD datasets.
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Technologies named here

  • GlimpseDNN
  • ASCADf
  • ASCADr
  • ASCADf_desync50
  • ASCADf_desync100

Techniques named here

  • Deep learning-based profiled Side-Channel Analysis
  • Stochastic leakage model
  • Layer-by-layer DNN explainability analysis
  • Pearson Correlation

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