A non-linear congruential pseudo random number generator

1986 ◽  
Vol 27 (1) ◽  
pp. 315-326 ◽  
Author(s):  
Jürgen Eichenauer ◽  
Jürgen Lehn
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Non Linear ◽  
Linear Congruential ◽  
Pseudo Random Number Generator

A multiple recursive non-linear congruential pseudo random number generator

1987 ◽  
Vol 59 (3) ◽  
pp. 331-346 ◽  
Author(s):  
Jürgen Eichenauer ◽  
Holger Grothe ◽  
Jürgen Lehn ◽  
Alev Topuzoğlu
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Non Linear ◽  
Linear Congruential ◽  
Pseudo Random Number Generator

scholarly journals Pseudo-random number generator based on linear congruence and delayed Fibonacci method

2021 ◽  
Author(s):  
Radosław Cybulski
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Test Machine ◽  
Number Generator ◽  
Chi Square ◽  
Pseudo Random Number ◽  
Congruential Method ◽  
Linear Congruential ◽  
Pseudo Random Number Generator

Pseudo-random number generation techniques are an essential tool to correctly test machine learning processes. The methodologies are many, but also the possibilities to combine them in a new way are plenty. Thus, there is a chance to create mechanisms potentially useful in new and better generators. In this paper, we present a new pseudo-random number generator based on a hybrid of two existing generators - a linear congruential method and a delayed Fibonacci technique. We demonstrate the implementation of the generator by checking its correctness and properties using chi-square, Kolmogorov and TestU01.1.2.3 tests and we apply the Monte Carlo Cross Validation method in classification context to test the performance of the generator in practice.

Fuzzy Based Pseudo Random Number Generator used for Wireless Networks

2013 ◽  
Vol 16 (2) ◽  
pp. 210-216 ◽  
Author(s):  
Sattar B. Sadkhan ◽  
◽  
Sawsan K. Thamer ◽  
Najwan A. Hassan ◽  
◽  
...  
Keyword(s):  
Wireless Networks ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator

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.

scholarly journals Magniber v2 Ransomware Decryption: Exploiting the Vulnerability of a Self-Developed Pseudo Random Number Generator

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 16
Author(s):  
Sehoon Lee ◽  
Myungseo Park ◽  
Jongsung Kim
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Operation Process ◽  
User Data ◽  
Encryption Algorithms ◽  
Recovery Result ◽  
Pseudo Random Number Generator ◽  
Randomness Tests

With the rapid increase in computer storage capabilities, user data has become increasingly important. Although user data can be maintained by various protection techniques, its safety has been threatened by the advent of ransomware, defined as malware that encrypts user data, such as documents, photographs and videos, and demands money to victims in exchange for data recovery. Ransomware-infected files can be recovered only by obtaining the encryption key used to encrypt the files. However, the encryption key is derived using a Pseudo Random Number Generator (PRNG) and is recoverable only by the attacker. For this reason, the encryption keys of malware are known to be difficult to obtain. In this paper, we analyzed Magniber v2, which has exerted a large impact in the Asian region. We revealed the operation process of Magniber v2 including PRNG and file encryption algorithms. In our analysis, we found a vulnerability in the PRNG of Magniber v2 developed by the attacker. We exploited this vulnerability to successfully recover the encryption keys, which was by verified the result in padding verification and statistical randomness tests. To our knowledge, we report the first recovery result of Magniber v2-infected files.

Reconfigurable chaotic pseudo random number generator based on FPGA

2019 ◽  
Vol 98 ◽  
pp. 174-180 ◽  
Author(s):  
Ahmed A. Rezk ◽  
Ahmed H. Madian ◽  
Ahmed G. Radwan ◽  
Ahmed M. Soliman
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Pseudo Random Number ◽  
Pseudo Random Number Generator
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