• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.742-746
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• 2010

In this paper, we propose new randomness testing methods based on approximate periods in order to improve the previous randomness testing method using exact pattern matching. Finding approximate periods of random sequences enables us to search similarly repeated parts, but it has disadvantages since it takes long time. In this paper we propose randomness testing methods whose time complexity is O($n^2$) by reducing the time complexity of computing approximate periods from O($n^3$) to O($n^2$). Moreover, we perform some experiments to compare pseudo random number generated by AES cryptographic algorithms and true random number.

• Journal of IKEEE
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• v.24 no.2
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• pp.543-549
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• 2020

As the importance of security increases in various fields, research on a random number generator (RNG) used for generating an encryption key, has been actively conducted. A high-quality RNG is essential to generate a high-performance encryption key, but the initial pseudo-random number generator (PRNG) has the possibility of predicting the encryption key from the outside even though a large amount of hardware resources are required to generate a sufficiently high-performance random number. Therefore, the demand of high-quality true random number generator (TRNG) generating random number through various noises is increasing. This paper examines and compares the representative TRNG methods based on metastable-based and ring-oscillator-based TRNGs. We compare the methods how the random sources are generated in each TRNG and evaluate its performances using NIST SP 800-22 tests.

• ETRI Journal
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• v.32 no.1
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• pp.93-101
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• 2010

A true random number generator (TRNG) is widely used to generate secure random numbers for encryption, digital signatures, authentication, and so on in crypto-systems. Since TRNG is vulnerable to environmental changes, a deterministic function is normally used to reduce bias and improve the statistical properties of the TRNG output. In this paper, we propose a linear corrector for secure TRNG. The performance of a linear corrector is bounded by the minimum distance of the corresponding linear error correcting code. However, we show that it is possible to construct a linear corrector overcoming the minimum distance limitation. The proposed linear corrector shows better performance in terms of removing bias in that it can enlarge the acceptable bias range of the raw TRNG output. Moreover, it is possible to efficiently implement this linear corrector using only XOR gates, which must have a suitable hardware size for embedded security systems.

• ETRI Journal
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• v.43 no.3
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• pp.571-571
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• 2021

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.268-275
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• 2020

A lightweight hardware design of self-timed ring based true random number generator (TRNG) suitable for information security applications is described. To reduce hardware complexity of TRNG, an entropy extractor with feedback structure was proposed, which minimizes the number of ring stages. The number of ring stages of the FSTR-TRNG was determined to be a multiple of eleven, taking into account operating clock frequency and entropy extraction circuit, and the ratio of tokens to bubbles was determined to operate in evenly-spaced mode. The hardware operation of FSTR-TRNG was verified by FPGA implementation. A set of statistical randomness tests defined by NIST 800-22 were performed by extracting 20 million bits of binary sequences generated by FSTR-TRNG, and all of the fifteen test items were found to meet the criteria. The FSTR-TRNG occupied 46 slices of Spartan-6 FPGA device, and it was implemented with about 2,500 gate equivalents (GEs) when synthesized in 180 nm CMOS standard cell library.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.635-639
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• 2021

As times goes by, a ton of electric devices have been developing. Nowadays, there are many personal electric goods that are connected each other and have important private information such as identification, account number, passwords, and so on. As many people own at least one electric device, security of the electric devices became significant. To prevent leakage of the information, study of Chaotic TRNG, "Chaotic True Random Number Generator", protecting the information by generating random numbers that are not able to be expected, is essential. In this paper, A chaotic TRNG is introduced is simulated. The proposed Chaotic TRNG is simulated with Virtuoso &, a circuit design program of Cadence that is a software company. For simulating the mentioned Chaotic TRNG, setting values, 0V low and 3V high on Vpulse, 1.2V on V-ref, 3.3V on VDD, and 0V on VSS, are used.

• Structural Engineering and Mechanics
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• v.34 no.5
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• pp.625-634
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• 2010

The objective of this paper is to apply a new proposed accuracy indicator to quantify the true and false modes for Eigensystem Realization Algorithm using output-based responses. First, a discrete mass-spring system and a simply supported continuous beam were modelled using finite element method. Then responses are simulated under random excitation. Natural Excitation Technique using only response measurements is applied to compute the impulse responses. Eigensystem Realization Algorithm is employed to identify the modal parameters on the simulated responses. A new accuracy indicator, Normalized Occurrence Number-NON, is developed to quantitatively partition the realized modes into true and false modes so that the false portions can be disregarded. Numerical simulation demonstrates that the new accuracy indicator can determine the true system modes accurately.

• Convergence Security Journal
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• v.24 no.2
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• pp.123-132
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• 2024

In the era of the Internet of Things, 7 billion diverse devices have been interconnected worldwide. Ensuring information security across these varied devices is crucial in this hyper-connected age. To achieve essential security functions such as confidentiality, integrity, and authentication, it is imperative to implement true random number generators (TRNGs). Therefore, this study proposes a method to rapidly characterize the randomness of TRNGs. While there are international standards for formally characterizing the randomness of TRNGs, adhering to these standards often requires significant time and resources. This study aims to help TRNG developers enhance efficiency in both time and cost by characterizing rough randomness and unpredictability. Firstly, we propose applying auto-correlation and cross-correlation metrics for analog signals. Secondly, we suggest adopting joint entropy and mutual information metrics for digital signals.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2603-2609
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• 2014

Reliability analysis of mechanical systems requires statistical modeling of input random variables such as distribution function types and statistical parameters that affect the performance of the mechanical systems. Some random variables are correlated, but considered as independent variables or wrong assumptions on input random variables have been used. In this paper, joint distributions were modeled using copulas and Bayesian method from limited number of data. To verify the proposed method, statistical simulation tests were carried out for various number of samples and correlation coefficients. As a result, the Bayesian method selected the most probable copula types among candidate copulas even though the candidate copula shapes are similar for low correlations or the number of data is limited. The most probable copulas also yielded similar reliabilities with the true reliability obtained from a true copula, so that it can be concluded that the Bayesian method provides accurate statistical modeling for the reliability analysis.