Publication detail

Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques

MRÁZEK, V. SEKANINA, L. DOBAI, R. SÝS, M. ŠVENDA, P.

Original Title

Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques

Type

journal article in Web of Science

Language

English

Original Abstract

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 randomness testing is proposed. The platform exploits the principles of genetic programming, which is a machine learning technique developed for 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. 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 which 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.

Keywords

randomness testing, evolvable hardware, FPGA

Authors

MRÁZEK, V.; SEKANINA, L.; DOBAI, R.; SÝS, M.; ŠVENDA, P.

Released

22. 11. 2019

ISBN

1063-8210

Periodical

IEEE Trans. on VLSI Systems.

Year of study

27

Number

12

State

United States of America

Pages from

2734

Pages to

2744

Pages count

11

URL

https://www.fit.vut.cz/research/publication/11687/

BibTex

@article{BUT161411,
  author="Vojtěch {Mrázek} and Lukáš {Sekanina} and Roland {Dobai} and Marek {Sýs} and Petr {Švenda}",
  title="Efficient On-Chip Randomness Testing Utilizing Machine Learning Techniques",
  journal="IEEE Trans. on VLSI Systems.",
  year="2019",
  volume="27",
  number="12",
  pages="2734--2744",
  doi="10.1109/TVLSI.2019.2923848",
  issn="1063-8210",
  url="https://www.fit.vut.cz/research/publication/11687/"
}

Documents

published.pdf
tvlsi19.pdf