Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders

  • Lichao Wu Delft University of Technology, The Netherlands
  • Stjepan Picek Delft University of Technology, The Netherlands
Keywords: side-channel analysis, Deep learning, Noise, Countermeasures, Denoising autoencoder

Abstract

In the profiled side-channel analysis, deep learning-based techniques proved to be very successful even when attacking targets protected with countermeasures. Still, there is no guarantee that deep learning attacks will always succeed. Various countermeasures make attacks significantly more complex, and such countermeasures can be further combined to make the attacks even more challenging. An intuitive solution to improve the performance of attacks would be to reduce the effect of countermeasures.
This paper investigates whether we can consider certain types of hiding countermeasures as noise and then use a deep learning technique called the denoising autoencoder to remove that noise. We conduct a detailed analysis of six different types of noise and countermeasures separately or combined and show that denoising autoencoder improves the attack performance significantly.

Published
2020-08-26
How to Cite
Wu, L., & Picek, S. (2020). Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2020(4), 389-415. https://doi.org/10.13154/tches.v2020.i4.389-415
Section
Articles