• The Journal of Society for e-Business Studies
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• v.6 no.2
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• pp.167-178
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• 2001

Key is generally formed using the Random Number. How to make the Random Number is to cast coin or dice as classical method, to form the Real Random Number with Hardware and to make the Pseudo Random Number by means of utilizing mathematical algorithm. This thesis presented NRNG(New Random Number Generator) which put self-development Hardware to use as Key Generation Method and inspected to compare the Real Random Number with the Pseudo Random Number and special properties which PRNG(Pseudo-Random Number Generator) creates.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.415-420
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• 2010

Cryptographic keys for security should be generated by true random number generators that apply irreversible hashing algorithms to initial values taken from a random source. As DRAM shows randomness in its access latency, it can be used as a random source. However, systems with synchronous DRAM (SDRAM) do not easily expose such randomness resulting in highly clustered random numbers. We resolved this problem by using the xor instruction. Statistical testing shows that the generated random bits have the quality comparable to true random bit sequences. The performance of bit generation is at the order of 100 Kbits/sec. Since the proposed random number generation requires neither external devices nor any special circuits, this method may be used in any computing device that employs DRAM.

• Journal of the Korean Data and Information Science Society
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• v.7 no.1
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• pp.105-112
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• 1996

Softwares including random number generation are abundant in modern informative society. But it's hard to get directly multivariate multinomial random numbers from existing softwares. Multivariate multinomial random numbers are greatly used in social and medical sciences. In this paper, we show that desired multivariate multinomial random numbers can be easily generated by the aids of existing random number generating software. Some characteristics of multivariate multinomial distribution are surveyd. Measures of association for the generated random numbers were computed and compared with population ones via simulation study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.803-806
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• 2007

Since the different characteristics from the PRNG (Pseudo Random Number Generator) or various deterministic devices such as arithmetic processing units, new concepts and test methods should be suggested in order to test TRNG (Ture Random Number Generator). Deterministic devices can be covered by ATPG (Automatic Test Pattern Generation), which uses patterns generated by cyclic shift registers due to its hardware oriented characteristics, pure random numbers are not possibly tested by automatic test pattern generation due to its analog-oriented characteristics. In this paper, we studied and analyzed a hardware/software combined test method named Diehard test, in which we apply continuous pattern variation to check the statistics. We also point out the considerations when making random number tests.

• Wind and Structures
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• v.3 no.1
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• pp.1-10
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• 2000

Recently, an efficient and practical method has been developed for the generation of univariate non-Gaussian wind pressure time histories on low building roofs; this methodology requires intermittent exponential random numbers for the simulation. On the other hand, the conventional spectral representation scheme with random phase is found suitable for the generation of univariate Gaussian wind pressure time histories on low building roofs; this simulation scheme requires uniform random numbers. The dependency of these simulation methodologies on the random number generator is one of the items affecting the accuracy of the simultion result; therefore, an attempt has been made to investigate the issue. This note presents the observed sensitivity of random number sets in repetitive simulations of Gaussian and non-Gaussian wind pressures.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.77-86
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• 2024

Using random numbers to represent uncertainty and unpredictability is essential in many industries. This is crucial in disciplines like computer science, cryptography, and statistics where the use of randomness helps to guarantee the security and dependability of systems and procedures. In computer science, random number generation is used to generate passwords, keys, and other security tokens as well as to add randomness to algorithms and simulations. According to recent research, the hardware random number generators used in billions of Internet of Things devices do not produce enough entropy. This article describes how raw data gathered by IoT system sensors can be used to generate random numbers for cryptography systems and also examines the results of these random numbers. The results obtained have been validated by successfully passing the FIPS 140-1 and NIST 800-22 test suites.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.1033-1036
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• 1988

This paper proposes a new method for generation of uniform random numbers using binary random sequences. These binary sequences are obtained from a de Bruijn sequence by random sampling method. Several statistical tests are carried out for the random numbers generated by the proposed method, and it is shown that the random numbers have good random properties.

• Journal of applied mathematics & informatics
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• v.4 no.2
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• pp.469-476
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• 1997

In this paper we present a random number generation method which is one of the rejection methods, To accelerate ratio-of-uniform method we use an efficiency variable γ. After finding the optimal value of γwith respect to interesting distribution with pro-portional density random numbers can be generated in acceleration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6161-6176
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• 2018

Pseudorandom numbers are useful in cryptographic operations for using as nonce, initial vector, secret key, etc. Security of the cryptosystem relies on the secret key parameters, so a good pseudorandom number is needed. In this paper, we have proposed a new approach for generation of pseudorandom number. This method uses the three dimensional combinational puzzle Rubik Cube for generation of random numbers. The number of possible combinations of the cube approximates to 43 quintillion. The large possible combination of the cube increases the complexity of brute force attack on the generator. The generator uses cryptographic hash function. Chaotic map is being employed for increasing random behavior. The pseudorandom sequence generated can be used for cryptographic applications. The generated sequences are tested for randomness using NIST Statistical Test Suite and other testing methods. The result of the tests and analysis proves that the generated sequences are random.

• Journal of KIISE
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• v.42 no.12
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• pp.1467-1473
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• 2015

In this paper, we implemented a parallel random number generation program on GPU's, which are known for high performance computing, using LCG (Linear Congruential Generator). Random numbers are important in all fields requiring the use of randomness, and LCG is one of the most widely used methods for the generation of pseudo-random numbers. We explained the parallel program using the NVIDIA CUDA model and MPI(Message Passing Interface) and showed uniform distribution and performance results. We also used a Monte Carlo algorithm to calculate pi(${\pi}$) comparing the parallel random number generator with cuRAND, which is a CUDA library function, and showed that our program is much more efficient. Finally we compared performance results using multi-GPU's with those of ideal speedups.