Comparison of Profiling Power Analysis Attacks Using Templates and Multi-Layer Perceptron Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU111090" target="_blank" >RIV/00216305:26220/14:PU111090 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Profiling Power Analysis Attacks Using Templates and Multi-Layer Perceptron Network

Popis výsledku v původním jazyce

In recent years, the cryptographic community has explored new approaches of power analysis based on machine learning models such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) or Random Forest (RF). Realized experiments proved that the method based on MLP can provide almost 100% success rate after optimization. Nevertheless, this description of results is based on the first order success rate that is not enough satisfactory because this value can be deceiving. Moreover, the power analysis method based on MLP has not been compared with other well-known approaches such as template attacks or stochastic attacks yet. In this paper, we introduce the first fair comparison of power analysis attacks based on MLP and templates. The comparison isaccomplished by using the identical data set and number of interesting points in power traces. We follow the unified framework for implemented side-channel attacks therefore we use guessing entropy as a metric of comparison.

Název v anglickém jazyce

Comparison of Profiling Power Analysis Attacks Using Templates and Multi-Layer Perceptron Network

Popis výsledku anglicky

In recent years, the cryptographic community has explored new approaches of power analysis based on machine learning models such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) or Random Forest (RF). Realized experiments proved that the method based on MLP can provide almost 100% success rate after optimization. Nevertheless, this description of results is based on the first order success rate that is not enough satisfactory because this value can be deceiving. Moreover, the power analysis method based on MLP has not been compared with other well-known approaches such as template attacks or stochastic attacks yet. In this paper, we introduce the first fair comparison of power analysis attacks based on MLP and templates. The comparison isaccomplished by using the identical data set and number of interesting points in power traces. We follow the unified framework for implemented side-channel attacks therefore we use guessing entropy as a metric of comparison.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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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

Proceedings of the 1st International Conference on Mathematical Methods & Computational Techniques in Science & Engineering

ISBN

978-1-61804-256-9

ISSN

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e-ISSN

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Počet stran výsledku

6

Strana od-do

134-139

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Atény

Datum konání akce

28. 11. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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