Publication detail

Optimization of Power Analysis Using Neural Network

MARTINÁSEK, Z. HAJNÝ, J. MALINA, L.

Original Title

Optimization of Power Analysis Using Neural Network

Czech Title

Optimalizace proudové analýzy využivající neuronovou síť

English Title

Optimization of Power Analysis Using Neural Network

Type

conference paper

Language

en

Original 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 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 abstract

Článek popisuje návrh a realizaci optimalizace proudové analýzy využívající klasické třívrstvé neuronové sítě k určení hodnoty tajného klíče uloženého v kryptografickém modulu. Navržená metoda klasifikuje tajný klíč po jednotlivých bajtech stejně jako metody diferenciální proudové analýzy. Výsledky klasifikace vytvořené neuronové sítě byly ověřeny 10-fold křížovou validací s $2560$ proudovými průběhy odpovídající operacím \texttt{AddRoudnKey} a \texttt{SubBytes} v inicializační fázi algoritmu AES (advanced encryption standard).

English 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 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.

RIV year

2014

Released

04.07.2014

Publisher

Springer

ISBN

978-3-319-08302-5

Book

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

Pages from

94

Pages to

107

Pages count

14

BibTex


@inproceedings{BUT108335,
  author="Zdeněk {Martinásek} and Jan {Hajný} and Lukáš {Malina}",
  title="Optimization of Power Analysis Using Neural Network",
  annote="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.",
  address="Springer",
  booktitle="Smart Card Research and Advanced Applications, Lecture Notes in Computer Science",
  chapter="108335",
  doi="10.1007/978-3-319-08302-5_7",
  howpublished="online",
  institution="Springer",
  year="2014",
  month="july",
  pages="94--107",
  publisher="Springer",
  type="conference paper"
}