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Optimization of Power Analysis Using Neural Network

  • Zdenek Martinasek
  • Jan Hajny
  • Lukas Malina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8419)

Abstract

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 was presented in [1] 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.

Keywords

Power analysis Neural network Optimization Preprocessing 

Notes

Acknowledgments

This research work is funded by the Ministry of Industry and Trade of the Czech Republic, project FR-TI4/647. Measurements were run on computational facilities of the SIX Research Center, registration number CZ.1.05/2.1.00/03.0072.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of TelecommunicationsBrno University of TechnologyBrnoCzech Republic

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