• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 1991.11a
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• pp.105-133
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• 1991

본 논문에서는 임의의 이진 난수발생기의 source가 $BMS_{p}$ 이거나 M-memory를 갖는 마르코프연쇄로 모델화 되었을 경우에 비트당 entropy와 관련이 있는 새로운 randomness에 관한 통계적 검정법을 제안한다. 기존에 알려진 이진 난수발생기의 randomness검정법이 0또는 1의 분포의 편향성(bias)이나 연속된 비트간의 상관성(correlation)중의 한 종류만의 non-randomness를 추적해낼 수 있는 반면에 새로운 검정법은 위의 두가지 검정을 통과하였을 때 암호학적으로 중요한 측도인 비트당 entropy 를 측정하여 암호학적인 약점을 검정할 수 있다. 또한 대칭(비밀키) 암호시스템의 통계적 결점을 바탕으로 하여 키를 찾는 공격자의 최적 전략( optimal strategy)문제를 분석하여 이 최적 전략이 이진 수열의 비트당 entropy와 밀접한 관계가 있음을 보이고 이 비트당 entropy와 관련이 있는 새로운 통계량을 도입하여 이진 난수 발생기의 source의 이진수열이 다음 3가지 경우, 즉, i.i.d. symmetric인 경우, $BMS_{p}$ 인 경우, M-memory를 갖는 마르코프연쇄인 경우의 각각에 대하여 특성을 조사하고 새로운 통계량의 평균과 분산을 구한다. 이때 구한 새로운 통계량은 잘 알려진 중심 극한 정리에 의하여 근사적으로 정규분포를 따르므로 위의 평균과 분산을 이용하여 스트림 암호시스템에서 구성요소로 많이 사용되는 몇 몇 간단한 이진 난수 발생기에 적용하여 통계적 검정을 실시함으로써 entropy 관점의 검정법이 새로운 randomness 검정법으로 타당함을 보인다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.2
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• pp.109-118
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• 2008

We present a new version of the first return time test for pseudorandomness. Let $R_n$ be the first return time of initial n-block with overlapping. An algorithm to calculate the probability distribution of the first return time $R_n$ for each starting block is presented and used to test pseudorandom number generators. The standard Z-test for log $R_n$ is applied to test the pseudorandom number generators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.41-45
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• 2002

From the paper which it sees a strong random number generator it uses a fuzzy set from 16 method of the statistical test which is a cryptograph random number test it verifies. 16 statistical test of NIST extends in crptograph and engineering whole it is a scale which is important distinguishes the distinction incapable characterstic of the random numbers which are used. To try introduce a fuzzy set the possibility of having a more strong randomness in order to be, it strengthens the function of the random number generator.

• Journal of the Korean Geotechnical Society
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• v.25 no.11
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• pp.87-103
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• 2009

To determine the reliable probabilistic distribution model of geotechnical properties, outlier and randomness test for analysis data, parameter estimation of probabilistic distribution model, and goodness-of-fit test for model parameter and probabilistic distribution model have to be performed in sequence. In this paper, the probabilistic distribution model's geotechnical properties of Songdo area in Incheon are estimated by the above proposed procedure. Also, the coefficient of variation (COV) representing the variability of geotechnical properties is determined for several geotechnical properties. Reliable probabilistic distribution model and COV of geotechnical properties can be used for probability-based design procedure and reasonable choice of design value in deterministic design method.

• 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.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.179-186
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• 2018

The number of devices connect to the internet is rapidly increasing with the advent of the IoT(Internet of Things). The IoT has improved the convenience of life. However, it makes security issues such as privacy violations. Therefore cybersecurity is the most important issue to be discussed nowadays. Especially, various protocols are used for same purpose due to rapidly increase of IoT market. To deal with this security threat noble vulnerability analysis is needed. In this paper, we contribute to the IoT security by proposing a new randomness-based test case evaluation methodology using variance and entropy. The test case evaluation method proposed in this paper can evaluate the test cases at a high speed regardless of the test set size, unlike the traditional technique.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.21-27
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• 2015

A conventional fire detection has been developed based on images captured from a fixed camera. However, It is difficult to apply current algorithms to a flying Quad-rotor to detect fire. To solve this problem, we propose that the fire detection algorithm can be modified for Quad-rotor using Ego-motion compensation. The proposed fire detection algorithm consists of color detection, motion detection, and fire determination using a randomness test. Color detection and randomness test are adapted similarly from an existing algorithm. However, Ego-motion compensation is adapted on motion detection for compensating the degree of Quad-rotor's motion using Planar Projective Transformation based on Optical Flow, RANSAC Algorithm, and Homography. By adapting Ego-motion compensation on the motion detection step, it has been proven that the proposed algorithm has been able to detect fires 83% of the time in hovering mode.

• Spatial Information Research
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• v.20 no.3
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• pp.39-50
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• 2012

The objective of this study is the quantitative analysis of climate change effects by performing several statistical analyses with hydrometeorological data sets for past 30 years in Geum river watershed. Temperature, precipitation, relative humidity data sets were collected from eight observation stations for 37 years(1973~2009) in Geum river watershed. River level data was collected from Gongju and Gyuam gauge stations for 36 years(1973~2008) considering rating curve credibility problems and future long-term runoff modeling. Annual and seasonal year-to-year variation of hydrometeorological components were analyzed by calculating the average, standard deviation, skewness, and coefficient of variation. The results show precipitation has the strongest variability. Run test, Turning point test, and Anderson Exact test were performed to check if there is randomness in the data sets. Temperature and precipitation data have randomness and relative humidity and river level data have regularity. Groundwater level data has both aspects(randomness and regularity). Linear regression and Mann-Kendal test were performed for trend test. Temperature is increasing yearly and seasonally and precipitation is increasing in summer. Relative humidity is obviously decreasing. The results of this study can be used for the evaluation of the effects of climate change on water resources and the establishment of future water resources management technique development plan.

• Journal of Korea Water Resources Association
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• v.31 no.6
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• pp.769-777
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• 1998

In this study, various time series are analyzed to check nonlinearities of the data. The nonlinearity of a system can be investigated by testing the randomness of the time series data. To test the randomness, four nonparametric test statistics and a new test statistic, called the BDS statistic are used and the results and the results are compared. The Brock, Dechert, and Scheinkman (BDS) statistic is originated from the statistical properties of the correlation integral which is used for searching for chaos and has been shown very effective in distinguishing nonlinear structures in dynamic systems from random structures. As a result of application to linear and nonlinear models which are well known, the BDS statistic is found to be more effective than nonparametric test statistics in identifying nonlinear structure in the time series. Hydrologic time series data are fitted to ARMA type models and the statistics are applied to the residuals. The results show that the BDS statistic can distinguish chaotic nonlinearity from randomness and that the BDS statistic can also be used for verifying the validity of the fitted model.