k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU116424" target="_blank" >RIV/00216305:26220/16:PU116424 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.13164/re.2016.0365" target="_blank" >http://dx.doi.org/10.13164/re.2016.0365</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/re.2016.0365" target="_blank" >10.13164/re.2016.0365</a>

Jazyk výsledku

angličtina

Název v původním jazyce

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

k-Nearest Neighbors Algorithm in Profiling Power Analysis Attacks

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LO1401" target="_blank" >LO1401: Interdisciplinární výzkum bezdrátových technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Radioengineering

ISSN

1210-2512

e-ISSN

—

Svazek periodika

1

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

19

Strana od-do

11-28

Kód UT WoS článku

000377231900020

EID výsledku v databázi Scopus

2-s2.0-85015616068