• Convergence Security Journal
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• v.15 no.7
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• pp.21-29
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• 2015

As the number of malicious Android applications increases rapidly, many automatic analysis systems are proposed. Hoping to trigger as many malicious behaviors as possible, the automatic analysis systems are adopting random touch generation modules. In this paper, we propose how to differentiate real human touches and randomly generated touches. Through experiments, we figured out that the distance between two consecutive human touches is shorter than that of random generation module. Also we found that the touch speed of human is also limited. In addition, humans rarely touch the outer area of smartphone screen. By using statistics of human smartphone touch, we developed an algorithm to differentiate between human touches and randomly generated touches. We hope this research will help enhance automatic Android app analysis systems.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1295-1299
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• 1990

A new method called random sampling method has been proposed for generation of binary random sequences. In this paper, a new concept, called merit factor Fn, is proposed for evaluating the randomness of the binary random sequences generated by the random sampling method. Using this merit factor Fn, some desirable conditions are investigated for uniform random numbers used in the random sampling method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.187-193
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• 2009

Dynamic response of an elastic pendulum system under random excitations was studied by using the Lagrangian equations of motion which uses the kinetic and potential energy of a target system. The responses of random excitations were calculated by using Monte Carl simulation which uses the series of random numbers. The procedure of Monte Carlo simulation is generation of random numbers, system model, system output, and statistical management of output. When the levels of random excitations were changed, the expected responses of the pendulum system showed various responses.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5080-5097
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• 2017

Channel characteristics-based secret key generation is an effective physical-layer security method. The issues of how to remove the effect of random noise and to balance the key generation rate (KGR) and the bit mismatch rate (BMR) are needed to be addressed. In this paper, to reduce the effect of random noise and extract more secret bits, a new quantization scheme with high key generation rate and low bit mismatch rate is proposed. In our proposed scheme, we try to use all measurements and correct the differences caused by noise at the boundary regions instead of simply dropping them. We evaluate and discuss the improvements of our proposed scheme. The results show that our proposed scheme achieves lower bit mismatch rate as well as remaining high key generation rate.

• 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.

• Journal of Korean Institute of Industrial Engineers
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• v.1 no.1
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• pp.87-92
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• 1975

Three approximation methods for generating outcomes on Poisson random variables are discussed. A comparison is made to determine which method requires the least computer execution time and to determine which is the most robust approximation. Results of the comparison study suggest the method to choose for the generating procedure depends on the mean value of Poisson random variable which is being generated.

• Journal of Internet of Things and Convergence
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• v.2 no.1
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• pp.23-27
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• 2016

There is Generated Moire fringe by fresnel diffraction that explains one of light's physical phenomenon and interference. In this paper, we propose to generate true random numbers by Moire fringe should be used by not pseudo-random number in cryptosystem.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.165-177
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• 1996

It is often needed to generate random numbers from truncated t-distributions to carry out Bayesian inferences, especially in Monte Carlo integration for estimation of posterior densities of constrained parameters. However, when the restricted area is an extreme tail area with a small probability most existing random generation methods are not efficient. In this paper, we propose an efficient acceptance-rejection method to generate random numbers from extreme tail areas of a t-distribution. Using some simulation results, we compare the proposed algorithm with other popular methods.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.75-80
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• 1999

This paper presents a new method to find a optimal solution for stochastic system. This method uses Genetic Algorithm(GA) and simulation. GA is used to search for new alternative and simulation is used to evaluate alternative. The stochastic system has one or more random variables as inputs. Random inputs lead to random outputs. Since the outputs are random, they can be considered only as estimates of the true characteristics of they system. These estimates could greatly differ from the corresponding real characteristics for the system. We need multiple replications to get reliable information on the system. And we have to analyze output data to get a optimal solution. It requires too much computation to be practical. We address the problem of reducing computation. The procedure on this paper use GA character, an iterative process, to reduce the number of replications. The same chromosomes could exit in post and present generation. Computation can be reduced by using the information of the same chromosomes which exist in post and present current generation.

• 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.