Publication Details
Evolving Boolean Functions for Fast and Efficient Randomness Testing
Sýs Marek (VUT)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Matyáš Václav, Dr. (CM-SFE)
Boolean function, genetic algorithm, statistical randomness testing
The security of cryptographic algorithms (such as block ciphers and hash functions) is often evaluated in terms of their output randomness. This paper presents a novel method for the statistical randomness testing of cryptographic primitives, which is based on the evolutionary construction of the so-called randomness distinguisher. Each distinguisher is represented as a Boolean polynomial in the Algebraic Normal Form. The previous approach, in which the distinguishers were developed in two phases by means of the brute-force method, is replaced with a more scalable evolutionary algorithm (EA). On seven complex datasets, this EA provided distinguishers of the same quality as the previous approach, but the execution time was in practice reduced 40 times. This approach allowed us to perform a more efficient search in the space of Boolean distinguishers and to obtain more complex high-quality distinguishers than the previous approach.
@inproceedings{BUT155018,
author="Vojtěch {Mrázek} and Marek {Sýs} and Zdeněk {Vašíček} and Lukáš {Sekanina} and Václav {Matyáš}",
title="Evolving Boolean Functions for Fast and Efficient Randomness Testing",
booktitle="Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18)",
year="2018",
pages="1302--1309",
publisher="Association for Computing Machinery",
address="Kyoto",
doi="10.1145/3205455.3205518",
isbn="978-1-4503-5618-3",
url="https://www.fit.vut.cz/research/publication/11686/"
}