MLP and CNN-based Classification of Points of Interest in Side-channel Attacks
- https://doi.org/10.2991/ijndc.k.200326.001How to use a DOI?
- Point of interest, forward difference, trace, multi-layer perceptron, convolutional neural network
A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks. We used public datasets from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks. Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces. The results show that two datasets are combined in different ways that improve the classification using neural networks. For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace. For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation.
- © 2020 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Hanwen Feng AU - Weiguo Lin AU - Wenqian Shang AU - Jianxiang Cao AU - Wei Huang PY - 2020 DA - 2020/04 TI - MLP and CNN-based Classification of Points of Interest in Side-channel Attacks JO - International Journal of Networked and Distributed Computing SP - 108 EP - 117 VL - 8 IS - 2 SN - 2211-7946 UR - https://doi.org/10.2991/ijndc.k.200326.001 DO - https://doi.org/10.2991/ijndc.k.200326.001 ID - Feng2020 ER -