TY - JOUR AU - Wu, Lichao AU - Picek, Stjepan PY - 2020/08/26 Y2 - 2024/03/28 TI - Remove Some Noise: On Pre-processing of Side-channel Measurements with Autoencoders JF - IACR Transactions on Cryptographic Hardware and Embedded Systems JA - TCHES VL - 2020 IS - 4 SE - Articles DO - 10.13154/tches.v2020.i4.389-415 UR - https://tches.iacr.org/index.php/TCHES/article/view/8688 SP - 389-415 AB - <p>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.<br>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.</p> ER -