• 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.

• Korean Journal of Mathematics
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• v.14 no.1
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• pp.85-93
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• 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
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• 2001.10a
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• pp.464-467
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• 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.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.164-168
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• 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
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• v.10 no.2
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• pp.25-32
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• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.878-881
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• 2009

As for the technology developed for network security, there is little difference of design ability between the domestic and the foreign studies. Although the development of 2048 RSA processor has been undergone, the processing speed does not meet the requirement due to its long width. These days, an RSA processor architecture with higher speed comsuming less resource is necessary. As for the development of RNG (Random Number Generator), the technology trend is moving from PRNG (Pseudo Random Number Generator) to TRNG (True Random Number Generator), also requiring less area and high speed.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1147-1152
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• 2012

In this paper, it is key issue about analysis of characteristics processing of digital random output stream of hardware random number generator, which is applied in medical area. The output stream of random number generator based on hardware binary random number is effected from factors such as delay, jitter, temperature, and so on. In this paper, it presents about major factor, which effects hardware output random number stream, and the randomness of output stream data, which are combined output stream and postprocessing data such as encryption algorithm, encoding algorithm, is analyzed. the analyzed results are evaluated by major test items of randomness.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.755-759
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• 2012

In this paper, it is about random number generator, which is based on hardware is utilized in medical science and game area. The Intel presents guideline of security level about hardware based true random number generator. At hardware based random number generator, the various test items, that are included in test suits as NIST statistical test, FIPS140-1, is applied. In this paper, it experiments about degree extent of randomness variation from filter scheme effects, which is applied in output stream of hardware noise source.

• ETRI Journal
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• v.42 no.4
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• pp.518-526
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• 2020

This paper presents an efficient hardware random-number generator based on a beta source. The proposed generator counts the values of "0" and "1" and provides a method to distinguish between pseudo-random and true random numbers by comparing them using simple cumulative operations. The random-number generator produces labeled data indicating whether the count value is a pseudo- or true random number according to its bit value based on the generated labeling data. The proposed method is verified using a system based on Verilog RTL coding and LabVIEW for hardware implementation. The generated random numbers were tested according to the NIST SP 800-22 and SP 800-90B standards, and they satisfied the test items specified in the standard. Furthermore, the hardware is efficient and can be used for security, artificial intelligence, and Internet of Things applications in real time.