Optimization of Power Analysis Using Neural Network

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Optimization of Power Analysis Using Neural Network
Original language description
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.
Czech name
—
Czech description
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Project
<a href="/en/project/FR-TI4%2F647" target="_blank" >FR-TI4/647: *Integration server with cryptographic protection</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Smart Card Research and Advanced Applications, Lecture Notes in Computer Science
ISBN
978-3-319-08302-5
ISSN
—
e-ISSN
—
Number of pages
14
Pages from-to
94-107
Publisher name
Springer
Place of publication
Neuveden
Event location
Berlín
Event date
Nov 27, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000348357900007

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