Optimization of Power Analysis Using Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU109604" target="_blank" >RIV/00216305:26220/14:PU109604 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-08302-5_7" target="_blank" >http://dx.doi.org/10.1007/978-3-319-08302-5_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-08302-5_7" target="_blank" >10.1007/978-3-319-08302-5_7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of Power Analysis Using Neural Network

Popis výsledku v původním jazyce

In power analysis, many different statistical methods and power consumption models are used to obtain the value of a secret key from the power traces measured. An interesting method of power analysis based on multi-layer perceptron claiming a $90%$ success rate. The theoretical and empirical success rates were determined to be $80%$ and $85%$, respectively, which is not sufficient enough. In the paper, we propose and realize an optimization of this power analysis method which improves the success rate to almost $100%$. The optimization is based on preprocessing the measured power traces using the calculation of the average trace and the subsequent calculation of the difference power traces. In this way, the prepared power patterns were used for neural network training and of course during the attack. This optimization is computationally undemanding compared to other methods of preprocessing usually applied in power analysis, and has a great impact on classification results. In the paper, we compare the results of the optimized method with the original implementation. We highlight positive and also some negative impacts of the optimization on classification results.

Název v anglickém jazyce

Optimization of Power Analysis Using Neural Network

Popis výsledku anglicky

In power analysis, many different statistical methods and power consumption models are used to obtain the value of a secret key from the power traces measured. An interesting method of power analysis based on multi-layer perceptron claiming a $90%$ success rate. The theoretical and empirical success rates were determined to be $80%$ and $85%$, respectively, which is not sufficient enough. In the paper, we propose and realize an optimization of this power analysis method which improves the success rate to almost $100%$. The optimization is based on preprocessing the measured power traces using the calculation of the average trace and the subsequent calculation of the difference power traces. In this way, the prepared power patterns were used for neural network training and of course during the attack. This optimization is computationally undemanding compared to other methods of preprocessing usually applied in power analysis, and has a great impact on classification results. In the paper, we compare the results of the optimized method with the original implementation. We highlight positive and also some negative impacts of the optimization on classification results.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/FR-TI4%2F647" target="_blank" >FR-TI4/647: *Integrační server s kryptografickým zabezpečením</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Smart Card Research and Advanced Applications, Lecture Notes in Computer Science

ISBN

978-3-319-08302-5

ISSN

—

e-ISSN

—

Počet stran výsledku

14

Strana od-do

94-107

Název nakladatele

Springer

Místo vydání

Neuveden

Místo konání akce

Berlín

Datum konání akce

27. 11. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000348357900007