• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.05a
• /
• pp.164-168
• /
• 2001

The Multiple Recursive Generator(MRG) has been considered by many scholars as a very good Random Number generator. For the long period and excellent statistical properties, the method of the combination with random number generators are used. In this paper, for two-combined MRGs, we examine the statistical properties and show the importance of the seeds likewise other random number generators. And we modify the two-combined MRGs and verify the statistical superiority.

• Journal of the Korea Society for Simulation
• /
• v.10 no.2
• /
• pp.25-32
• /
• 2001

The Multiple Recursive Generator(MRG) has been considered by many scholars as a very good random number generator. For the long period md excellent statistical properties, the method of the combination with random number generators is used. In this paper, we thought the two-combined MRGs. Using the frequency and serial test, and runs test, we studied the importance of the initial seeds likewise other random number generators.

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

• Korean Journal of Mathematics
• /
• v.14 no.1
• /
• pp.85-93
• /
• 2006

A good arithmetic random number generator should possess full period, uniformity and independence, etc. To obtain the excellent random number generator, many researchers have found good parameters. Also an initial seed is the important factor in random number generator. But, there is no theoretical guideline for using the initial seeds. Therefore, random number generator is usually used with the arbitrary initial seed. Through the empirical tests, we show that the choice of the initial values for the seed is important to generate good random numbers.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.10a
• /
• pp.464-467
• /
• 2001

A good arithmetic random number generator should possess full period, uniformity and independence, etc. To obtain the excellent random number generator, many researchers have found good parameters. Also an initial seed is the important factor in random number generator. But, there is no theoretical guideline for using the initial seeds. Therefore, random number generator is usually used with the arbitrary initial seed. Through the empirical tests, we show that the choice of the initial values for the seed is important to generate good random numbers.

• Journal of the Korean Mathematical Society
• /
• v.50 no.4
• /
• pp.879-897
• /
• 2013

Shannon entropy is one of the widely used randomness measures especially for cryptographic applications. However, the conventional entropy tests are less sensitive to the inter-bit dependency in random samples. In this paper, we propose new online randomness test schemes for true random number generators (TRNGs) based on the mutual information between consecutive ${\kappa}$-bit output blocks for testing of inter-bit dependency in random samples. By estimating the block entropies of distinct lengths at the same time, it is possible to measure the mutual information, which is closely related to the amount of the statistical dependency between two consecutive data blocks. In addition, we propose a new estimation method for entropies, which accumulates intermediate values of the number of frequencies. The proposed method can estimate entropy with less samples than Maurer-Coron type entropy test can. By numerical simulations, it is shown that the new proposed scheme can be used as a reliable online entropy estimator for TRNGs used by cryptographic modules.

• Journal of the Korean Data and Information Science Society
• /
• v.9 no.2
• /
• pp.237-246
• /
• 1998

We put the pseudo-random number generator into catagories like MiCG, MuCG, URG, ICG, EICG, and test uniformity and independency by 10,000 times through n empirical trial after selecting this random number generator. Here, from a fraction of data(20, 40, 60, 80, 100) with a significance level of 0.1, 0.05 and 0.01, we drive cumulative frequency with K-S, $X^{2}$, poker, run, autocorrelation test. As a result from the uniformity and independency among five random number generators based on all these data, all random number generator except EICG passed uniformity and independency test, and the URG turn out to be excellent in periodicity.

• Journal of the Korean Data and Information Science Society
• /
• v.26 no.3
• /
• pp.593-610
• /
• 2015

Adaptive Rejection Sampling (ARS) method is a well-known random number generator to acquire a random sample from a probability distribution, and has the advantage of improving the proposal distribution during the sampling procedures, which update it closer to the target distribution. However, the use of ARS is limited since it can be used only for the target distribution in the form of the log-concave function, and thus various methods have been proposed to overcome such a limitation of ARS. In this paper, we attempt to compare five random number generators based on ARS in terms of adequacy and efficiency. Based on empirical analysis using simulations, we discuss their results and make a comparison of five ARS-based methods.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.4
• /
• pp.415-420
• /
• 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 Korea Institute of Information Security & Cryptology
• /
• v.11 no.6
• /
• pp.67-76
• /
• 2001

In this paper, we investigate criteria for evaluating cryptographic strength based on randomness testing of the advanced encryption standard candidates, which have conducted by NIST(National Institute of Standards & Technology). It is difficult to prove that a given cryptographic algorithm meets sufficient conditions or requirements for provable security. The statistical testing of random number generators is one of methods to evaluate cryptographic strength and is based on statistical properties of random number generators. We apply randomness testing on several cryptographic algorithms that have not been tested by NIST and find criteria for evaluating cryptographic strength from the results of randomness testing. We investigate two criteria, one is the number of rejected samples and the other is the p-value from p-values of the samples.