An Effective Statistical Test Suite for Pseudorandom number Generators in Digital Signatures and Security Robustness Evaluation including a Wavelet Test for Randomness

2020 ◽  
Author(s):  
Dimitrios A. Karras
Keyword(s):  
Digital Signatures ◽  
Statistical Test ◽  
Test Suite ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generators ◽  
Test For Randomness

scholarly journals A statistical test suite for random and pseudorandom number generators for cryptographic applications

2000 ◽  
Author(s):  
Andrew Rukhin ◽  
Juan Sota ◽  
James Nechvatal ◽  
Miles Smid ◽  
Elaine Barker ◽  
...  
Keyword(s):  
Statistical Test ◽  
Test Suite ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generators

scholarly journals Using Poly-Dragon Cryptosystem in a Pseudorandom Number Generator Mstg

2014 ◽  
Vol 61 (1) ◽  
pp. 105-116
Author(s):  
Viliam Hromada ◽  
Milan Vojvoda
Keyword(s):  
Finite Groups ◽  
Statistical Test ◽  
Statistical Properties ◽  
Public Key ◽  
Test Suite ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Public Key Cryptosystem ◽  
Number Generator

Abstract This paper deals with a new pseudorandom number generator MSTg proposed in 2010. Its construction is based on random covers for finite groups. We have used a public-key cryptosystem Poly-Dragon to generate these random covers and have studied the statistical properties of the resulting pseudorandom number generator by testing its output using the NIST Statistical Test Suite

scholarly journals On pseudorandom number generators

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 128
Author(s):  
Daniel Chicayban Bastos ◽  
Luis Antonio Brasil Kowada ◽  
Raphael C. S. Machado
Keyword(s):  
Programming Languages ◽  
Random Number ◽  
Statistical Tests ◽  
Statistical Test ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Statistical Sampling ◽  
Simple Task ◽  
Random Number Generators ◽  
Pseudorandom Number Generators

<p class="Abstract">Statistical sampling and simulations produced by algorithms require fast random number generators; however, true random number generators are often too slow for the purpose, so pseudorandom number generators are usually more suitable. But choosing and using a pseudorandom number generator is no simple task; most pseudorandom number generators fail statistical tests. Default pseudorandom number generators offered by programming languages usually do not offer sufficient statistical properties. Testing random number generators so as to choose one for a project is essential to know its limitations and decide whether the choice fits the project’s objectives. However, this study presents a reproducible experiment that demonstrates that, despite all the contributions it made when it was first published, the popular NIST SP 800-22 statistical test suite as implemented in the software package is inadequate for testing generators.</p>

Unreliability of Available Pseudorandom Number Generators

2020 ◽  
Vol 60 (11) ◽  
pp. 1747-1753
Author(s):  
A. A. Belov ◽  
N. N. Kalitkin ◽  
M. A. Tintul
Keyword(s):  
Pseudorandom Number ◽  
Pseudorandom Number Generators

Pseudorandom Number Generators Based on Chaotic Dynamical Systems

2001 ◽  
Vol 08 (02) ◽  
pp. 137-146 ◽  
Author(s):  
Janusz Szczepański ◽  
Zbigniew Kotulski
Keyword(s):  
Dynamical Systems ◽  
Communication Systems ◽  
Initial Conditions ◽  
Pseudorandom Number ◽  
New Approach ◽  
Chaotic Dynamical Systems ◽  
Pseudorandom Number Generators ◽  
Transmission Of Information ◽  
Extreme Sensitivity ◽  
Contemporary Technology

Pseudorandom number generators are used in many areas of contemporary technology such as modern communication systems and engineering applications. In recent years a new approach to secure transmission of information based on the application of the theory of chaotic dynamical systems has been developed. In this paper we present a method of generating pseudorandom numbers applying discrete chaotic dynamical systems. The idea of construction of chaotic pseudorandom number generators (CPRNG) intrinsically exploits the property of extreme sensitivity of trajectories to small changes of initial conditions, since the generated bits are associated with trajectories in an appropriate way. To ensure good statistical properties of the CPRBG (which determine its quality) we assume that the dynamical systems used are also ergodic or preferably mixing. Finally, since chaotic systems often appear in realistic physical situations, we suggest a physical model of CPRNG.

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