Design of a cryptographically secure pseudo random number generator with grammatical evolution
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Ryan:2022:SciRep,
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author = "Conor Ryan and Meghana Kshirsagar and Gauri Vaidya and
Andrew Cunningham and R. Sivaraman",
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title = "Design of a cryptographically secure pseudo random
number generator with grammatical evolution",
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journal = "Scientific Reports",
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year = "2022",
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volume = "12",
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pages = "Article number: 8602",
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
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URL = "https://rdcu.be/cUXIn",
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DOI = "doi:10.1038/s41598-022-11613-x",
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size = "10 pages",
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abstract = "This work investigates the potential for using
Grammatical Evolution (GE) to generate an initial seed
for the construction of a pseudo-random number
generator (PRNG) and cryptographically secure (CS)
PRNG. We demonstrate the suitability of GE as an
entropy source and show that the initial seeds exhibit
an average entropy value of 7.940560934 for 8-bit
entropy, which is close to the ideal value of 8. We
then construct two random number generators, GE-PRNG
and GE-CSPRNG, both of which employ these initial
seeds. We use Monte Carlo simulations to establish the
efficacy of the GE-PRNG using an experimental setup
designed to estimate the value for pi, in which
100,000,000 random numbers were generated by our
system. This returned the value of pi of 3.146564000,
which is precise up to six decimal digits for the
actual value of pi. We propose a new approach called
control_flow_incrementor to generate cryptographically
secure random numbers. The random numbers generated
with CSPRNG meet the prescribed National Institute of
Standards and Technology SP800-22 and the Diehard
statistical test requirements. We also present a
computational performance analysis of GE-CSPRNG
demonstrating its potential to be used in industrial
applications.",
- }
Genetic Programming entries for
Conor Ryan
Meghana Kshirsagar
Gauri Vaidya
Andrew Cunningham
R Sivaraman
Citations