Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147153
Title: Non-profiled side-channel attack based on deep learning using picture trace
Authors: Won, Yoo-Seung
Han, Dong-Guk
Jap, Dirmanto
Bhasin, Shivam
Park, Jong-Yeon
Keywords: Engineering::Computer science and engineering::Information systems::Information systems applications
Issue Date: 2021
Source: Won, Y., Han, D., Jap, D., Bhasin, S. & Park, J. (2021). Non-profiled side-channel attack based on deep learning using picture trace. IEEE Access, 9, 22480-22492. https://dx.doi.org/10.1109/ACCESS.2021.3055833
Journal: IEEE Access 
Abstract: Over the years, deep learning algorithms have advanced a lot and any innovation in the algorithms are demonstrated and benchmarked for image classification. Several other field including side-channel analysis (SCA) have recently adopted deep learning with great success. In SCA, the deep learning algorithms are typically working with 1-dimensional (1-D) data. In this work, we propose a unique method to improve deep learning based side-channel analysis by converting the measurements from raw-trace of 1-dimension data based on float or byte data into picture-formatted trace that has information based on the data position. We demonstrate why 'Picturization' is more suitable for deep learning and compare how input and hidden layers interact for each raw (1-D) and picture form. As one potential application, we use a Binarized Neural Network (BNN) learning method that relies on a BNN's natural properties to improve variables. In addition, we propose a novel criterion for attack success or failure based on statistical confidence level rather than determination of a correct key using a ranking system.
URI: https://hdl.handle.net/10356/147153
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2021.3055833
Rights: © 2021 The Author(s). Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:TL Journal Articles

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