• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3A
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• pp.221-230
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• 2002

This paper presents a new moving object segmentation algorithm using markov random field. The algorithm is based on signal detection theory. That is to say, motion of moving object is decided by binary decision rule, and false decision is corrected by markov random field model. The procedure toward complete segmentation consists of two steps: motion detection and object segmentation. First, motion detection decides the presence of motion on velocity vector by binary decision rule. And velocity vector is generated by optical flow. Second, object segmentation cancels noise by Bayes rule. Experimental results demonstrate the efficiency of the presented method.

• Bioinformatics and Biosystems
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• v.1 no.1
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• pp.67-72
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• 2006

The aim of this paper is to discuss the effect of missing values in detecting differentially expressed genes in a cDNA microarray experiment in the context of a one sample problem. We conducted a cDNA micro array experiment to detect differentially expressed genes for the metastasis of colorectal cancer based on twenty patients who underwent liver resection due to liver metastasis from colorectal cancer. Total RNAs from metastatic liver tumor and adjacent normal liver tissue from a single patient were labeled with cy5 and cy3, respectively, and competitively hybridized to a cDNA microarray with 7775 human genes. We used $M=log_2(R/G)$ for the signal evaluation, where Rand G denoted the fluorescent intensities of Cy5 and Cy3 dyes, respectively. The statistical problem comprises a one sample test of testing E(M)=0 for each gene and involves multiple tests. The twenty cDNA microarray data would comprise a matrix of dimension 7775 by 20, if there were no missing values. However, missing values occur for various reasons. For each gene, the no missing proportion (NMP) was defined to be the proportion of non-missing values out of twenty. In detecting differentially expressed (DE) genes, we used the genes whose NMP is greater than or equal to 0.4 and then sequentially increased NMP by 0.1 for investigating its effect on the detection of DE genes. For each fixed NMP, we imputed the missing values with K-nearest neighbor method (K=10) and applied the nonparametric t-test of Dudoit et al. (2002), SAM by Tusher et al. (2001) and empirical Bayes procedure by $L\ddot{o}nnstedt$ and Speed (2002) to find out the effect of missing values in the final outcome. These three procedures yielded substantially agreeable result in detecting DE genes. Of these three procedures we used SAM for exploring the acceptable NMP level. The result showed that the optimum no missing proportion (NMP) found in this data set turned out to be 80%. It is more desirable to find the optimum level of NMP for each data set by applying the method described in this note, when the plot of (NMP, Number of overlapping genes) shows a turning point.

• Journal of Korea Water Resources Association
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• v.46 no.1
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• pp.13-24
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• 2013

The main objective of this study was to develop a new regional frequency analysis model based on hierarchical Bayesian model that allows us to better estimate and quantify model parameters as well as their associated uncertainties. A Monte-carlo experiment procedure has been set up to verify the proposed regional frequency analysis. It was found that the proposed hierarchical Bayesian model based regional frequency analysis outperformed the existing L-moment based regional frequency analysis in terms of reducing biases associated with the model parameters. Especially, the bias is remarkably decreased with increasing return period. The proposed model was applied to six weather stations in Jeollabuk-do, and compared with the existing L-moment approach. This study also provided shrinkage process of the model parameters that is a typical behavior in hierarchical Bayes models. The results of case study show that the proposed model has the potential to obtain reliable estimates of the parameters and quantitatively provide their uncertainties.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2575-2583
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• 1998

The productionof the highly relible softwae systems and theirs performance evaluation hae become important interests in the software industry. The software evaluation has been mainly carried out in ternns of both reliability and performance of software system. Software reliability is the probability that no software error occurs for a fixed time interval during software testing phase. These theoretical software reliability models are sometimes unsuitable for the practical testing phase in which a software error at a certain testing stage occurs by causes of the imperfect debugging, abnornal software correction, and so on. Such a certatin software testing stage needs to be considered as an outlying stage. And we can assume that the software reliability does not improve by means of muisance factor in this outlying testing stage. In this paper, we discuss Bavesian software reliability growth modeling and estimation procedure in the presence of an imidentitied outlying software testing stage by the modification of Jehnski Moranda. Also we derive the Bayes estimaters of the software reliability panmeters by the assumption of prior information under the squared error los function. In addition, we evaluate the proposed software reliability growth model with an unidentified outlying stage in an exchangeable model according to the values of nuisance paramether using the accuracy, bias, trend, noise metries as the quantilative evaluation criteria through the compater simulation.