Publication detail
k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks
MARTINÁSEK, Z. ZEMAN, V. MALINA, L. MARTINÁSEK, J.
Original Title
k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks
English Title
k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks
Type
journal article in Web of Science
Language
en
Original Abstract
Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP). In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first contains the power traces of an unprotected AES (Advanced Encryption Standard) implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). The obtained results revealed some interesting facts, namely, an elementary \textit{k}-NN (\textit{k}-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.
English abstract
Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards. In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP). In this paper, we made an extensive comparison of machine learning algorithms in the power analysis. For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy. In our research, we used three datasets, the first contains the power traces of an unprotected AES (Advanced Encryption Standard) implementation. The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4). The obtained results revealed some interesting facts, namely, an elementary \textit{k}-NN (\textit{k}-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.
Keywords
Power Analysis; Machine Learning; Template Attack; Comparison; Smart Cards
Released
01.06.2016
ISBN
1210-2512
Periodical
Radioengineering
Year of study
1
Number
1
State
CZ
Pages from
11
Pages to
28
Pages count
19
Documents
BibTex
@article{BUT118680,
author="Zdeněk {Martinásek} and Václav {Zeman} and Lukáš {Malina} and Josef {Martinásek}",
title="k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks",
annote="Power analysis presents the typical example of successful attacks against trusted cryptographic devices such as RFID (Radio-Frequency IDentifications) and contact smart cards.
In recent years, the cryptographic community has explored new approaches in power analysis based on machine learning models such as Support Vector Machine (SVM), RF (Random Forest) and Multi-Layer Perceptron (MLP).
In this paper, we made an extensive comparison of machine learning algorithms in the power analysis.
For this purpose, we implemented a verification program that always chooses the optimal settings of individual machine learning models in order to obtain the best classification accuracy.
In our research, we used three datasets, the first contains the power traces of an unprotected AES (Advanced Encryption Standard) implementation.
The second and third datasets are created independently from public available power traces corresponding to a masked AES implementation (DPA Contest v4).
The obtained results revealed some interesting facts, namely, an elementary \textit{k}-NN (\textit{k}-Nearest Neighbors) algorithm, which has not been commonly used in power analysis yet, shows great application potential in practice.
",
chapter="118680",
doi="10.13164/re.2016.0365",
howpublished="print",
number="1",
volume="1",
year="2016",
month="june",
pages="11--28",
type="journal article in Web of Science"
}