• Journal of Internet Computing and Services
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• v.6 no.1
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• pp.27-38
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• 2005

Recently, the rapidly development of computing environments and the spread of Internet make possible to obtain and use of information easily. Immediately, by opposition function the Hacker's unlawful intrusion and threats rise for network environments as time goes on. Specially, the internet consists of Unix and TCP/IP had many vulnerability. the security techniques of authentication and access controls cannot adequate to solve security problem, thus IDS developed with 2nd defence line. In this paper, intrusion detection method using Bayesian Networks estimated probability values of behavior contexts based on Bayes theory. The contexts of behaviors or events represents Bayesian Networks of graphic types. We profiled concisely normal behaviors using behavior context. And this method be able to detect new intrusions or modificated intrusions. We had simulation using DARPA 2000 Intrusion Data.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.1-11
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• 2018

Multivariate process control has become important in various applied fields. For instance, there are many situations in which the simultaneous monitoring of multivariate quality characteristics is necessary for the manufacturing industry. Despite its importance, its practical usage is not as convenient because it is difficult to identify the source of the out-of-control signal in a multivariate control chart. In this paper, we will introduce how to detect the source of the out-of-control by using confidence intervals for new observations, and will discuss the identification and interpretation of the out-of-control variable through simulation studies.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.811-823
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• 2018

Multivariate control chart based on Hotelling's $T^2$ statistic is a powerful tool in statistical process control for identifying an out-of-control process. It is used to monitor multiple process characteristics simultaneously. Detection of the out-of-control signal with the $T^2$ chart indicates mean vector shifts. However, these multivariate signals make it difficult to interpret the cause of the out-of-control signal. In this paper, we review methods of signal interpretation based on the Mason, Young, and Tracy (MYT) decomposition of the $T^2$ statistic. We also provide an example on how to implement it using R software and demonstrate simulation studies for comparing the performance of these methods.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

• Journal of the korean academy of Pediatric Dentistry
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• v.29 no.4
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• pp.600-606
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• 2002

The purpose of this study was to evaluate the diagnostic validity of an incipient occlusal caries using argon laser fluorescence. Extracted human premolars and molars with enamel carious lesion of occlusal surface were assessed using visual examination, visual examination with probing, argon laser fluorescence and histologic depth of carious lesion. The results in each of all the three detection methods were compared to the assessment of histologic depth of carious lesion using polarized microscope. The results from the present study can be summarized as follows; 1. There was highly correlation between the histologic depth of occlusal caries and all three detection methods(P<0.01). 2. The reproducibility(kappa value) of the visual examination, visual examination with probing and argon laser fluorescence between the histologic depth of occlusal caries was 0.189, 0.128, 0.472. The highest correlation was seen between detection of occlusal caries by argon laser fluorescence and histologic scores by polarized microscope. The results from this study indicated that argon laser fluorescence considered to be accurate and reliable method in detecting occlusal caries.

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.41-59
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• 2015

Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.4
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• pp.701-713
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• 2021

Race Condition is a vulnerability in which two or more processes input or manipulate a common resource at the same time, resulting in unintended results. This vulnerability can lead to problems such as denial of service, elevation of privilege. When a vulnerability occurs in software, the relevant information is documented, but often the cause of the vulnerability or the source code is not disclosed. In this case, analysis at the binary level is necessary to detect the vulnerability. This paper aims to detect the Time-Of-Check Time-Of-Use (TOCTOU) Race Condition vulnerability of UNIX kernel-based File System at the binary level. So far, various detection techniques of static/dynamic analysis techniques have been studied for the vulnerability. Existing vulnerability detection tools using static analysis detect through source code analysis, and there are currently few studies conducted at the binary level. In this paper, we propose a method for detecting TOCTOU Race Condition in File System based on Control Flow Graph and Call Graph through Binary Analysis Platform (BAP), a binary static analysis tool.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.4
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• pp.384-394
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• 2020

This paper proposes an outlier detection model based on machine learning that can diagnose the presence or absence of major engine parts through unsupervised learning analysis of main engine big data of a ship. Engine big data of the ship was collected for more than seven months, and expert knowledge and correlation analysis were performed to select features that are closely related to the operation of the main engine. For unsupervised learning analysis, ensemble model wherein many predictive models are strategically combined to increase the model performance, is used for anomaly detection. As a result, the proposed model successfully detected the anomalous engine status from the normal status. To validate our approach, clustering analysis was conducted to find out the different patterns of anomalies the anomalous point. By examining distribution of each cluster, we could successfully find the patterns of anomalies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.557-559
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• 2022

The use of open source has the advantage that the development environment is convenient and maintenance is easier, but there is a limitation in that it is easy to be exposed to vulnerabilities from a security point of view. In this regard, the LOG4J vulnerability, which is an open source logging library widely used in Apache, was recently discovered. Currently, the risk of this vulnerability is at the 'highest' level, and developers are using it in many systems without being aware of such a problem, so there is a risk that hacking accidents due to the LOG4J vulnerability will continue to occur in the future. In this paper, we analyze the LOG4J vulnerability in detail and propose a SNORT detection policy technology that can detect vulnerabilities more quickly and accurately in the security control system. Through this, it is expected that in the future, security-related beginners, security officers, and companies will be able to efficiently monitor and respond quickly and proactively in preparation for the LOG4J vulnerability.