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 88 articles )

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  1. Wang, Xiao-Qing; Zhang, Hao; Sun, Yu-Jie; Wang, Xing-Yuan: A plaintext-related image encryption algorithm based on compressive sensing and a novel hyperchaotic system (2021)
  2. Zhang, Sen; Zheng, Jiahao; Wang, Xiaoping; Zeng, Zhigang: Multi-scroll hidden attractor in memristive HR neuron model under electromagnetic radiation and its applications (2021)
  3. Huang, Chunguang; Ding, Qun: Performance of finite precision on discrete chaotic map based on a feedback shift register (2020)
  4. Lorek, Paweł; Łoś, Grzegorz; Gotfryd, Karol; Zagórski, Filip: On testing pseudorandom generators via statistical tests based on the arcsine law (2020)
  5. Rainer, Benjamin; Pilz, Jürgen; Deutschmann, Martin: Assessing the statistical quality of RNGs (2020)
  6. Zhang, Sen; Wang, Xiaoping; Zeng, Zhigang: A simple no-equilibrium chaotic system with only one signum function for generating multidirectional variable hidden attractors and its hardware implementation (2020)
  7. Alawida, Moatsum; Samsudin, Azman; Teh, Je Sen: Enhancing unimodal digital chaotic maps through hybridisation (2019)
  8. Bernard, Florent; Haddad, Patrick; Fischer, Viktor; Nicolai, Jean: From physical to stochastic modeling of a TERO-based TRNG (2019)
  9. Elmanfaloty, Rania A.; Abou-Bakr, Ehab: Random property enhancement of a 1D chaotic PRNG with finite precision implementation (2019)
  10. Fan, Chunlei; Ding, Qun; Tse, Chi K.: Counteracting the dynamical degradation of digital chaos by applying stochastic jump of chaotic orbits (2019)
  11. Han, Dandan; Min, Lequan; Zang, Hongyan; Yang, Xiuping: Robust chaos of cubic polynomial discrete maps with application to pseudorandom number generators (2019)
  12. Pain, Puspak; Das, Kunal; Sadhu, Arindam; Kanjilal, Maitreyi Ray; De, Debashis: Novel true random number generator based hardware cryptographic architecture using quantum-dot cellular automata (2019)
  13. Panwar, Kirtee; Purwar, Ravindra Kumar; Jain, Anchal: Cryptanalysis and improvement of a color image encryption scheme based on DNA sequences and multiple 1D chaotic maps (2019)
  14. Uğuz, Muhiddin; Doğanaksoy, Ali; Sulak, Fatih; Koçak, Onur: R-2 composition tests: a family of statistical randomness tests for a collection of binary sequences (2019)
  15. Wang, Chuanfu; Ding, Qun: Constructing digitized chaotic time series with a guaranteed enhanced period (2019)
  16. Wang, Xing-Yuan; Li, Zhi-Ming: A stream/block combination image encryption algorithm using logistic matrix to scramble (2019)
  17. 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)
  18. 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)
  19. Li, Jie; Zheng, Jianliang; Whitlock, Paula: Efficient deterministic and non-deterministic pseudorandom number generation (2018)
  20. Lin, Y.; Wang, F.; Liu, B.: Random number generators for large-scale parallel Monte Carlo simulations on FPGA (2018)

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