Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques.

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00107545" target="_blank" >RIV/00216224:14330/19:00107545 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216305:26230/19:PU134941

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TVLSI.2019.2923848" target="_blank" >http://dx.doi.org/10.1109/TVLSI.2019.2923848</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TVLSI.2019.2923848" target="_blank" >10.1109/TVLSI.2019.2923848</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques.

Popis výsledku v původním jazyce

Randomness testing is an important procedure that bit streams, produced by critical cryptographic primitives such as encryption functions and hash functions, have to undergo. In this paper, a new hardware platform for the randomness testing is proposed. The platform exploits the principles of genetic programming, which is a machine learning technique developed for the automated program and circuit design. The platform is capable of evolving efficient randomness distinguishers directly on a chip. Each distinguisher is represented as a Boolean polynomial in the algebraic normal form. The randomness testing is conducted for bit streams that are either stored in an on-chip memory or generated by a circuit placed on the chip. The platform is developed with a Xilinx Zynq-7000 All Programmable System on Chip that integrates a field programmable gate array with on-chip ARM processors. The platform is evaluated in terms of the quality of randomness testing, performance, and resources utilization. With power budget less than 3 W, the platform provides comparable randomness testing capabilities with the standard testing batteries running on a personal computer.

Název v anglickém jazyce

Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques.

Popis výsledku anglicky

Randomness testing is an important procedure that bit streams, produced by critical cryptographic primitives such as encryption functions and hash functions, have to undergo. In this paper, a new hardware platform for the randomness testing is proposed. The platform exploits the principles of genetic programming, which is a machine learning technique developed for the automated program and circuit design. The platform is capable of evolving efficient randomness distinguishers directly on a chip. Each distinguisher is represented as a Boolean polynomial in the algebraic normal form. The randomness testing is conducted for bit streams that are either stored in an on-chip memory or generated by a circuit placed on the chip. The platform is developed with a Xilinx Zynq-7000 All Programmable System on Chip that integrates a field programmable gate array with on-chip ARM processors. The platform is evaluated in terms of the quality of randomness testing, performance, and resources utilization. With power budget less than 3 W, the platform provides comparable randomness testing capabilities with the standard testing batteries running on a personal computer.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA16-08565S" target="_blank" >GA16-08565S: Rozvoj kryptoanalytických metod prostřednictvím evolučních výpočtů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

ISSN

1063-8210

e-ISSN

1557-9999

Svazek periodika

27

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

2734-2744

Kód UT WoS článku

000508360300004

EID výsledku v databázi Scopus

2-s2.0-85069509602