NIST Statistical Test Suite

A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may be 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 be 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 be useful as a first step in determining whether or not a generator is suitable for a particular cryptographic application. However, no set of statistical tests can absolutely certify a generator as appropriate for usage in a particular application, i.e., statistical testing cannot serve as a substitute for cryptanalysis. The design and cryptanalysis of generators is outside the scope of this paper.

References in zbMATH (referenced in 57 articles )

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  1. Bakiri, Mohammed; Guyeux, Christophe; Couchot, Jean-François; Oudjida, Abdelkrim Kamel: Survey on hardware implementation of random number generators on FPGA: theory and experimental analyses (2018)
  2. Hofmann, Marko; Meyer-Nieberg, Silja: Time to dispense with the (p)-value in OR? Rationale and implications of the statement of the American statistical association (ASA) on (p)-values (2018)
  3. Lin, Y.; Wang, F.; Liu, B.: Random number generators for large-scale parallel Monte Carlo simulations on FPGA (2018)
  4. Liu, Lingfeng; Liu, Bocheng; Hu, Hanping; Miao, Suoxia: Reducing the dynamical degradation by bi-coupling digital chaotic maps (2018)
  5. Saito, Asaki; Yamaguchi, Akihiro: Pseudorandom number generator based on the Bernoulli map on cubic algebraic integers (2018)
  6. Wang, Chuanfu; Fan, Chunlei; Feng, Kai; Huang, Xin; Ding, Qun: Analysis of the time series generated by a new high-dimensional discrete chaotic system (2018)
  7. Zhang, Yingqian; Wang, Xingyuan; Liu, Liyan; Liu, Jia: Fractional order spatiotemporal chaos with delay in spatial nonlinear coupling (2018)
  8. Chen, E.; Min, Lequan; Chen, Guanrong: Discrete chaotic systems with one-line equilibria and their application to image encryption (2017)
  9. Cho, Kenichiro; Miyano, Takaya: Design and test of pseudorandom number generator using a star network of Lorenz oscillators (2017)
  10. Dastgheib, Mohammad A.; Farhang, Mahmoud: A digital pseudo-random number generator based on sawtooth chaotic map with a guaranteed enhanced period (2017)
  11. Liu, Yunqi; Luo, Yuling; Song, Shuxiang; Cao, Lvchen; Liu, Junxiu; Harkin, Jim: Counteracting dynamical degradation of digital chaotic Chebyshev map via perturbation (2017)
  12. Ma, Yuan; Lin, Jingqiang; Jing, Jiwu: On the entropy of oscillator-based true random number generators (2017)
  13. Mikhailov, Eugeniy E.: Programming with MATLAB for scientists. A beginner’s introduction (2017)
  14. Sýs, Marek; Říha, Zdeněk; Matyáš, Vashek: Algorithm 970: Optimizing the NIST statistical test suite and the Berlekamp-Massey algorithm (2017)
  15. Takeda, Yuichi; Huzii, Mituaki; Watanabe, Norio; Kamakura, Toshinari: An improved method for identification of patterns in the non-overlapping template matching test (2017)
  16. Vigna, Sebastiano: Further scramblings of Marsaglia’s (\mathsfxorshift) generators (2017)
  17. Fatemi-Behbahani, Esmaeil; Ansari-Asl, Karim; Farshidi, Ebrahim: A new approach to analysis and design of chaos-based random number generators using algorithmic converter (2016) ioport
  18. Faure, Emil V.; Shcherba, Anatoly I.; Rudnytskyi, V. M.: The method and criterion for quality assessment of random number sequences (2016)
  19. Girardin, Valérie; Regnault, Philippe: Escort distributions minimizing the Kullback-Leibler divergence for a large deviations principle and tests of entropy level (2016)
  20. Han, Dandan; Min, Lequan; Chen, Guanrong: A stream encryption scheme with both key and plaintext avalanche effects for designing chaos-based pseudorandom number generator with application to image encryption (2016)

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