Detail publikace

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

MARTINÁSEK, Z. ZEMAN, V. MALINA, L. MARTINÁSEK, J.

Originální název

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Anglický název

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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"
}