Remove Some Noise

On Pre-processing of Side-channel Measurements with Autoencoders

Journal Article (2020)
Author(s)

Lichao Wu (TU Delft - Cyber Security)

Stjepan Picek (TU Delft - Cyber Security)

Research Group
Cyber Security
Copyright
© 2020 L. Wu, S. Picek
DOI related publication
https://doi.org/10.13154/tches.v2020.i4.389-415
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 L. Wu, S. Picek
Research Group
Cyber Security
Issue number
4
Volume number
2020
Pages (from-to)
389-415
Reuse Rights

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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.