MLP and CNN-based Classification of Points of Interest in Side-channel Attacks

DOI

10.2991/ijndc.k.200326.001How to use a DOI?

Keywords

Point of interest; forward difference; trace; multi-layer perceptron; convolutional neural network

Abstract

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.

Copyright

© 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/).

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TY  - JOUR
AU  - Hanwen Feng
AU  - Weiguo Lin
AU  - Wenqian Shang
AU  - Jianxiang Cao
AU  - Wei Huang
PY  - 2020
DA  - 2020/04/17
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  - 10.2991/ijndc.k.200326.001
ID  - Feng2020
ER  -