scholarly journals Yet Another Pseudorandom Number Generator

2017 ◽  
Vol 63 (2) ◽  
pp. 195-199 ◽  
Author(s):  
Borislav Stoyanov ◽  
Krzysztof Szczypiorski ◽  
Krasimir Kordov
Keyword(s):  
Boolean Function ◽  
Data Security ◽  
Statistical Tests ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Number Generation ◽  
Pseudorandom Number Generation ◽  
High Level

Abstract We propose a novel pseudorandom number generator based on R¨ossler attractor and bent Boolean function. We estimated the output bits properties by number of statistical tests. The results of the cryptanalysis show that the new pseudorandom number generation scheme provides a high level of data security.

scholarly journals A New Two-Dimensional Mutual Coupled Logistic Map and Its Application for Pseudorandom Number Generator

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xuan Huang ◽  
Lingfeng Liu ◽  
Xiangjun Li ◽  
Minrong Yu ◽  
Zijie Wu
Keyword(s):  
Statistical Tests ◽  
Chaotic Map ◽  
Logistic Map ◽  
Security Analysis ◽  
Three Dimensional ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Two Dimensional ◽  
Practical Applications

Given that the sequences generated by logistic map are unsecure with a number of weaknesses, including its relatively small key space, uneven distribution, and vulnerability to attack by phase space reconstruction, this paper proposes a new two-dimensional mutual coupled logistic map, which can overcome these weaknesses. Our two-dimensional chaotic map model is simpler than the recently proposed three-dimensional coupled logistic map, whereas the sequence generated by our system is more complex. Furthermore, a new kind of pseudorandom number generator (PRNG) based on the mutual coupled logistic maps is proposed for application. Both statistical tests and security analysis show that our proposed PRNG has good randomness and that it can resist all kinds of attacks. The algorithm speed analysis indicates that PRNG is valuable to practical applications.

Design and Test of Pseudorandom Number Generator Using a Star Network of Lorenz Oscillators

2017 ◽  
Vol 27 (12) ◽  
pp. 1750184 ◽  
Author(s):  
Kenichiro Cho ◽  
Takaya Miyano
Keyword(s):  
Stream Cipher ◽  
Statistical Tests ◽  
New Method ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Lorenz Equations ◽  
Dynamical Models ◽  
Pseudorandom Numbers ◽  
Star Network

We have recently developed a chaos-based stream cipher based on augmented Lorenz equations as a star network of Lorenz subsystems. In our method, the augmented Lorenz equations are used as a pseudorandom number generator. In this study, we propose a new method based on the augmented Lorenz equations for generating binary pseudorandom numbers and evaluate its security using the statistical tests of SP800-22 published by the National Institute for Standards and Technology in comparison with the performances of other chaotic dynamical models used as binary pseudorandom number generators. We further propose a faster version of the proposed method and evaluate its security using the statistical tests of TestU01 published by L’Ecuyer and Simard.

scholarly journals CONFIGURABLE CELLULAR AUTOMATA FOR PSEUDORANDOM NUMBER GENERATION

2005 ◽  
Vol 16 (07) ◽  
pp. 1051-1073 ◽  
Author(s):  
MARIE THERESE QUIETA ◽  
SHENG-UEI GUAN
Keyword(s):  
Cellular Automata ◽  
Random Number ◽  
Random Number Generation ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
New Approach ◽  
Suitable Candidate ◽  
Number Generation ◽  
A Cell ◽  
Pseudorandom Number Generation

This paper proposes a generalized structure of cellular automata (CA) — the configurable cellular automata (CoCA). With selected properties from programmable CA (PCA) and controllable CA (CCA), a new approach to cellular automata is developed. In CoCA, the cells are dynamically reconfigured at run-time via a control CA. Reconfiguration of a cell simply means varying the properties of that cell with time. Some examples of properties to be reconfigured are rule selection, boundary condition, and radius. While the objective of this paper is to propose CoCA as a new CA method, the main focus is to design a CoCA that can function as a good pseudorandom number generator (PRNG). As a PRNG, CoCA can be a suitable candidate as it can pass 17 out of 18 Diehard tests with 31 cells. CoCA PRNG's performance based on Diehard test is considered superior over other CA PRNG works. Moreover, CoCA opens new rooms for research not only in the field of random number generation, but in modeling complex systems as well.

scholarly journals Random Walk in a N-Cube Without Hamiltonian Cycle to Chaotic Pseudorandom Number Generation: Theoretical and Practical Considerations

2017 ◽  
Vol 27 (01) ◽  
pp. 1750014 ◽  
Author(s):  
Sylvain Contassot-Vivier ◽  
Jean-François Couchot ◽  
Christophe Guyeux ◽  
Pierre-Cyrille Heam
Keyword(s):  
Uniform Distribution ◽  
Hamiltonian Cycle ◽  
Chaotic Behavior ◽  
Statistical Tests ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Expected Length ◽  
Obvious Consequence ◽  
Pseudorandom Number Generation

Designing a pseudorandom number generator (PRNG) is a difficult and complex task. Many recent works have considered chaotic functions as the basis of built PRNGs: the quality of the output would indeed be an obvious consequence of some chaos properties. However, there is no direct reasoning that goes from chaotic functions to uniform distribution of the output. Moreover, embedding such kind of functions into a PRNG does not necessarily allow to get a chaotic output, which could be required for simulating some chaotic behaviors. In a previous work, some of the authors have proposed the idea of walking into a [Formula: see text]-cube where a balanced Hamiltonian cycle has been removed as the basis of a chaotic PRNG. In this article, all the difficult issues observed in the previous work have been tackled. The chaotic behavior of the whole PRNG is proven. The construction of the balanced Hamiltonian cycle is theoretically and practically solved. An upper bound of the expected length of the walk to obtain a uniform distribution is calculated. Finally practical experiments show that the generators successfully pass the classical statistical tests.

scholarly journals Beyond Statistical Analysis in Chaos-Based CSPRNG Design

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
John Prakash Arockiasamy ◽  
Lydia Elizabeth Benjamin ◽  
Rhymend Uthariaraj Vaidyanathan
Keyword(s):  
Statistical Analysis ◽  
Statistical Tests ◽  
Security Analysis ◽  
Statistical Testing ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Pseudorandom Sequences ◽  
Randomness Test ◽  
Modern Cryptography

The design of cryptographically secure pseudorandom number generator (CSPRNG) producing unpredictable pseudorandom sequences robustly and credibly has been a nontrivial task. Almost all the chaos-based CSPRNG design approaches invariably depend only on statistical analysis. Such schemes designed to be secure are being proven to be predictable and insecure day by day. This paper proposes a design and instantiation approach to chaos-based CSPRNG using proven generic constructions of modern cryptography. The proposed design approach with proper instantiation of such generic constructions eventually results in providing best of both worlds that is the provable security guarantees of modern cryptography and passing of necessary statistical tests as that of chaos-based schemes. Also, we introduce a new coupled map lattice based on logistic-sine map for the construction of CSPRNG. The proposed pseudorandom number generator is proven using rigorous security analysis as that of modern cryptography and tested using the standard statistical testing suites. It is observed that the generated sequences pass all stringent statistical tests such as NIST, Dieharder, ENT, and TestU01 randomness test suites.

A Test of Minitab's Pseudorandom-Number Generator

1986 ◽  
Vol 63 (3) ◽  
pp. 1319-1322
Author(s):  
F. Richard Ferraro
Keyword(s):  
Statistical Tests ◽  
Pseudorandom Number ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Educational Settings ◽  
Computer Package ◽  
Standard Tests

The reliability of Minitab's pseudorandom-number generator was investigated. Minitab, an interactive statistical computer package, allows the user a variety of statistical tests and analyses. Standard tests of randomness were performed; results indicated that numbers generated by Minitab are suitably random for use in business or educational settings.

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