A Statistical Analysis on In-Built Pseudo Random Number Generators Using NIST Test Suite

Hegadi, Rajendra; Patil, Abhijit Prakash

doi:10.1109/icccs49678.2020.9276849

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, the following test indexes in NIST SP800-22 Test suite 12 are selected for randomness detection of sequences generated using the scheme proposed in this paper. Firstly, the single bit frequency test (frequency mono bit test), which tests the proportion of 0 and 1 in the sequence, the distribution of 0 and 1 is uniform, which is the basic requirement of the sequence as a key.…”

Section: Randomness Testmentioning

confidence: 99%

Research on the pseudorandom sequence generator based on compertz map and piecewise map

Zhang¹,

Qin²,

Zhou

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

View full text Add to dashboard Cite

For the purpose of improving the phenomenon of short period of the sequence result in using single chaotic map, this paper proposes a pseudorandom sequence generator scheme based on Compertz map and piecewise map. The sequence generated by our scheme passed the National Institute of Standards and Technology (NIST) randomness tests, compared with other schemes, the sequence generated by our scheme shows good randomness and statistical characteristics. In the aspect of application, the sequence generated by our scheme can be use as pseudorandom numbers and secret key. In terms of confidentiality, the seed key consist of the combination of control parameters of piecewise map, initial value of chaotic system, number of initial iteration rounds of chaotic map and left shift of key sequence. In the aspect of security, the secret key generating by our scheme will cost a lot for the attacker to crack.

show abstract

Section: Randomness Testmentioning

confidence: 99%

Research on the pseudorandom sequence generator based on compertz map and piecewise map

Zhang¹,

Qin²,

Zhou

2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

View full text Add to dashboard Cite

show abstract

“…It allows producing high-quality random sequences at low costs making it very useful in cryptography [3]. It is rather like a long recurring decimal number, but it repeats itself every desired bit [4]. PRBS is not a truly random sequence but with long sequence lengths and the most important point is its generation is rather simple [5].…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Performance of a Second-order and a Third-order RLC Circuit of PRBS Generator

Faudzi

Zhang

2021

JIAE

View full text Add to dashboard Cite

A pseudo-binary random signal (PRBS) has been widely utilized for system identification in complex signals to develop an experimental approach. PRBS generator is a circuit that generates pseudo-random numbers. This work aims to analyze the best fit value of the PRBS generator with second-order and third-order under-damped black-box RLC circuit of the estimated model. The procedures conducting here can be divided into three parts. First, to design two black boxes using the RLC circuit representing a critically under-damped second-order and third-order system. PRBS generated with maximum-length sequence (MLS) equals 127 bits by using seven shift registers. Second, simulate the PRBS generator using MATLAB software and validate the estimated model from the simulation using the System Identification Tool in MATLAB. Next, connecting hardware RLC circuit and reading input and output signals using an oscilloscope. Finally, 2500 samples of captured data were used for estimation. Then, analyze and compare the best fit of the simulation and experiment with second-order and third-order under-damped black-box RLC circuit. Furthermore, analyze and compare best fit using different sample time. The results showed that the best fit of the second-order model with under-damped black-box RLC circuit was autoregressive with the exogenous term (ARX) 211, where the best fit of the simulation was 99.88%, and the best fit of the experiment was 96.04%. And the results showed that the best fit of the third-order model with an under-damped black-box RLC circuit was ARX 331, where the best fit of the simulation was 99%, and the best fit of the experiment was 94.28%. It was concluded that the best fit value of the second-order was better than the third order. What’s more, the results showed that when the select range is the same, the bigger the sample time, the better the best fit.

show abstract

Exploring the potential of deep learning and machine learning techniques for randomness analysis to enhance security on IoT

Ince

2023

Int. J. Inf. Secur.

View full text Add to dashboard Cite

A Statistical Analysis on In-Built Pseudo Random Number Generators Using NIST Test Suite

Cited by 3 publications

References 10 publications

Research on the pseudorandom sequence generator based on compertz map and piecewise map

Research on the pseudorandom sequence generator based on compertz map and piecewise map

Analysis on the Performance of a Second-order and a Third-order RLC Circuit of PRBS Generator

Exploring the potential of deep learning and machine learning techniques for randomness analysis to enhance security on IoT

Contact Info

Product

Resources

About