A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications

By Andrew L. Rukhin, Juan Soto, James Nechvatal, M. Smid, Elaine B. Barker. 2000

4,480 citations recorded by the source corpus.

Abstract : This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may he used in many cryptographic applications, such as the generation of key material. Generators suitable for use in cryptographic applications may need to meet stronger requirements than for other applications. In particular, their outputs must he unpredictable in the absence of knowledge of the inputs. Some criteria for characterizing and selecting appropriate generators are discussed in this document. The subject of statistical testing and its relation to cryptanalysis is also discussed, and some recommended statistical tests are provided. These tests may he useful as a first step in determining whether or not a generator is suitable for a particular cryptographic application. The design and cryptanalysis of generators is outside the scope of this paper.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/961fc0f6c68303cde94a28fdb24e6eaf3ef040b4

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Citations in the corpus

Handbook of Monte Carlo Methods (2011, methodology): “2], and adapted version of the Diehard suite of tests by Marsaglia [28], the ones implemented by the National Institute of Standards and Technology (NIST) [37], and various other tests.”
Random Number Generation (2008, background): “We detail some of them after discussing measures of uniformity.”
TestU01: A C library for empirical testing of random number generators (2006, methodology): “Besides TestU01, the best-known public-domain statistical testing packages for RNGs are DIEHARD [Marsaglia 1996] and the test suite implemented by the National Institute of Standards and Technology (NIST) of the USA [Rukhin et al. 2001].”
Key Generation From Wireless Channels: A Review (2016, methodology): “A statistical randomness test suite provided by National Institute of Standards and Technology (NIST) [46] is widely used to test the randomness of random number generators (RNGs) and pseudo random number generators (PRNGs).”
Fast physical random bit generation with chaotic semiconductor lasers (2008, background): “Efforts are being made to develop faster and more reliable generators and establish better standards for random number generation based on stringent tests of randomnes”
Power-Up SRAM State as an Identifying Fingerprint and Source of True Random Numbers (2009, positive): “While min-entropy is intended as the primary assurance of randomness for the extracted bits, these bits are also tested using the runs, approximate entropy, and block frequency tests from the NIST suite [37].”
Cosine-transform-based chaotic system for image encryption (2019, methodology): “The National Institute of Standards and Technology (NIST) SP800-22 is an established criterion for measuring the randomness of data sequence [21] .”
Digital image steganography: Survey and analysis of current methods (2010, neutral): “26] has answered the question “how random is the noise?” qualitatively there are various methods which estimate randomness quantitatively (see [101]).”
Complex photonics: Dynamics and applications of delay-coupled semiconductors lasers (2013, methodology): “Prior to the deterministic mechanism as described by Sano (1994), Henry and Kazarinov (1986) had introduced an explanation for the LFF, describing it as a noise-induced escape phenomenon from one of the stable high-gain modes.”
A novel true random number generator based on a stochastic diffusive memristor (2017, methodology): “To assess the performance of our diffusive memristor TRNG, we carried out randomness testing for 76 million binary bits using the standard statistical testing package developed by the National Institute of Standards and Technology (NIST 800-22 test suite) 32 .”
Significant-Loophole-Free Test of Bell's Theorem with Entangled Photons. (2015, background): “[30–35], for more information about the experimental setup and data analysis.”