CLC number: TP309
On-line Access: 2017-04-12
Received: 2015-12-19
Revision Accepted: 2016-02-28
Crosschecked: 2017-03-28
Cited: 0
Clicked: 7732
Ehsan Saeedi, Yinan Kong, Md. Selim Hossain. Side-channel attacks and learning-vector quantization[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 511-518.
@article{title="Side-channel attacks and learning-vector quantization",
author="Ehsan Saeedi, Yinan Kong, Md. Selim Hossain",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="4",
pages="511-518",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500460"
}
%0 Journal Article
%T Side-channel attacks and learning-vector quantization
%A Ehsan Saeedi
%A Yinan Kong
%A Md. Selim Hossain
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 4
%P 511-518
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500460
TY - JOUR
T1 - Side-channel attacks and learning-vector quantization
A1 - Ehsan Saeedi
A1 - Yinan Kong
A1 - Md. Selim Hossain
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 4
SP - 511
EP - 518
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1500460
Abstract: The security of cryptographic systems is a major concern for cryptosystem designers, even though cryptography algorithms have been improved. side-channel attacks, by taking advantage of physical vulnerabilities of cryptosystems, aim to gain secret information. Several approaches have been proposed to analyze side-channel information, among which machine learning is known as a promising method. Machine learning in terms of neural networks learns the signature (power consumption and electromagnetic emission) of an instruction, and then recognizes it automatically. In this paper, a novel experimental investigation was conducted on field-programmable gate array (FPGA) implementation of elliptic curve cryptography (ECC), to explore the efficiency of side-channel information characterization based on a learning vector quantization (LVQ) neural network. The main characteristics of LVQ as a multi-class classifier are that it has the ability to learn complex non-linear input-output relationships, use sequential training procedures, and adapt to the data. Experimental results show the performance of multi-class classification based on LVQ as a powerful and promising approach of side-channel data characterization.
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