PSEUDO RANDOM NUMBER GENERATOR BASED ON SYNCHRONIZED CHAOTIC MAPS

2010 ◽  
Vol 21 (02) ◽  
pp. 275-290 ◽  
Author(s):  
AFSHIN AKHSHANI ◽  
SOHRAB BEHNIA ◽  
AMIR AKHAVAN ◽  
SIEW-CHOO LIM ◽  
ZAINURIAH HASSAN
Keyword(s):  
Random Number ◽  
Chaotic Maps ◽  
Chaotic Map ◽  
Random Number Generator ◽  
Random Numbers ◽  
Random Generator ◽  
Coupled Maps ◽  
The Individual ◽  
Test Suites ◽  
Pseudo Random Number Generator

In this paper, a hierarchy of coupled maps in synchronized state is introduced. Then some discussions about the individual properties of these chaotic maps are presented, from a dynamical systems viewpoint. Also, one of these chaotic map is used as a Nonlinear Pseudo Random Generator (NPRNG). This paper addresses the chaotic features of this map which are useful for generating nonlinear pseudo random numbers. Results of the analysis and extensive tests such as the NIST, DIEHARD and ENT test suites indicate that the NPRNG exhibits good statistical randomness properties.

scholarly journals A Hardware Pseudo-Random Number Generator Using Stochastic Computing and Logistic Map

Micromachines ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Junxiu Liu ◽  
Zhewei Liang ◽  
Yuling Luo ◽  
Lvchen Cao ◽  
Shunsheng Zhang ◽  
...  
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Logistic Map ◽  
Random Number Generator ◽  
Alternative Methods ◽  
Random Numbers ◽  
Number Generator ◽  
Stochastic Computing ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

Recent research showed that the chaotic maps are considered as alternative methods for generating pseudo-random numbers, and various approaches have been proposed for the corresponding hardware implementations. In this work, an efficient hardware pseudo-random number generator (PRNG) is proposed, where the one-dimensional logistic map is optimised by using the perturbation operation which effectively reduces the degradation of digital chaos. By employing stochastic computing, a hardware PRNG is designed with relatively low hardware utilisation. The proposed hardware PRNG is implemented by using a Field Programmable Gate Array device. Results show that the chaotic map achieves good security performance by using the perturbation operations and the generated pseudo-random numbers pass the TestU01 test and the NIST SP 800-22 test. Most importantly, it also saves 89% of hardware resources compared to conventional approaches.

PSEUDO-RANDOM NUMBER GENERATOR BASED ON COUPLED MAP LATTICES

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2409-2414 ◽  
Author(s):  
HUAPING LÜ ◽  
SHIHONG WANG ◽  
GANG HU
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Random Number Generator ◽  
Coupled Map Lattices ◽  
Random Numbers ◽  
Secure Communications ◽  
Number Generator ◽  
Generator System ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

A one-way coupled chaotic map lattice is used for generating pseudo-random numbers. It is shown that with suitable cooperative applications of both chaotic and conventional approaches, the output of the spatiotemporally chaotic system can easily meet the practical requirements of random numbers, i.e., excellent random statistical properties, long periodicity of computer realizations, and fast speed of random number generations. This pseudo-random number generator system can be used as ideal synchronous and self-synchronizing stream cipher systems for secure communications.

scholarly journals Cryptographically Secure Pseudo-Random Number Generator IP-Core Based on SHA2 Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1869 ◽  
Author(s):  
Luca Baldanzi ◽  
Luca Crocetti ◽  
Francesco Falaschi ◽  
Matteo Bertolucci ◽  
Jacopo Belli ◽  
...  
Keyword(s):  
High Performance ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Random Numbers ◽  
Generation Process ◽  
Number Generator ◽  
Ip Core ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

In the context of growing the adoption of advanced sensors and systems for active vehicle safety and driver assistance, an increasingly important issue is the security of the information exchanged between the different sub-systems of the vehicle. Random number generation is crucial in modern encryption and security applications as it is a critical task from the point of view of the robustness of the security chain. Random numbers are in fact used to generate the encryption keys to be used for ciphers. Consequently, any weakness in the key generation process can potentially leak information that can be used to breach even the strongest cipher. This paper presents the architecture of a high performance Random Number Generator (RNG) IP-core, in particular a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) IP-core, a digital hardware accelerator for random numbers generation which can be employed for cryptographically secure applications. The specifications used to develop the proposed project were derived from dedicated literature and standards. Subsequently, specific architecture optimizations were studied to achieve better timing performance and very high throughput values. The IP-core has been validated thanks to the official NIST Statistical Test Suite, in order to evaluate the degree of randomness of the numbers generated in output. Finally the CSPRNG IP-core has been characterized on relevant Field Programmable Gate Array (FPGA) and ASIC standard-cell technologies.

scholarly journals A Novel True Random Number Generator Based on Mouse Movement and a One-Dimensional Chaotic Map

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Xingyuan ◽  
Qin Xue ◽  
Teng Lin
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Random Number Generator ◽  
Random Numbers ◽  
Number Generator ◽  
Initial Value ◽  
One Dimensional ◽  
True Random Number Generator ◽  
Mouse Movement ◽  
Real Value

We propose a novel true random number generator using mouse movement and a one-dimensional chaotic map. We utilize thex-coordinate of the mouse movement to be the length of an iteration segment of our TRNs and they-coordinate to be the initial value of this iteration segment. And, when it iterates, we perturb the parameter with the real value produced by the TRNG itself. And we find that the TRNG we proposed conquers several flaws of some former mouse-based TRNGs. At last we take experiments and test the randomness of our algorithm with the NIST statistical test suite; results illustrate that our TRNG is suitable to produce true random numbers (TRNs) on universal personal computers (PCs).

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

2021 ◽  
Author(s):  
Conor Ryan ◽  
Meghana Kshirsagar ◽  
Gauri Vaidya ◽  
Andrew Cunningham ◽  
R Sivaraman
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Grammatical Evolution ◽  
Random Numbers ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Initial Seed ◽  
Discernible Difference ◽  
Pseudo Random Number Generator ◽  
Entropy Source

Abstract This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG.The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve.

scholarly journals A New Pseudo-Random Number Generator Based on Two Chaotic Maps

Informatica ◽  
2013 ◽  
Vol 24 (2) ◽  
pp. 181-197 ◽  
Author(s):  
Michael Francois ◽  
Thomas Grosges ◽  
Dominique Barchiesi ◽  
Robert Erra
Keyword(s):  
Random Number ◽  
Chaotic Maps ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator
Sign in / Sign up

Export Citation Format

Share Document