• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.2
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• pp.99-106
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• 2003

Intrusions pose a serious security risk in a network environment. For detecting the intrusion effectively, many researches have developed data mining framework for constructing intrusion detection modules. Traditional anomaly detection techniques focus on detecting anomalies in new data after training on normal data. To detect anomalous behavior, Precise normal Pattern is necessary. This training data is typically expensive to produce. For this, the understanding of the characteristics of data on network is inevitable. In this paper, we propose to use clustering and association rules as the basis for guiding anomaly detection. For applying entropy to filter noisy data, we present a technique for detecting anomalies without training on normal data. We present dynamic transaction for generating more effectively detection patterns.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.4
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• pp.29-39
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• 2002

In general, the intrusion detection method of anomalous behaviors has high false alarm rate which contains false-positive and false-negative. To increase the sensitivity of intrusion detection, we propose a method of aggregate detection to reduce false alarm rate by using correlation between misuse activity detection sensors and anomalous ones. For each normal behavior and anomalous one, we produce the reflection rate between the result from one sensor and another in off-line. Then, we apply this rate to the result of real-time detection to reduce false alarm rate.

• ETRI Journal
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• v.44 no.4
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• pp.654-671
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• 2022

Accidental falls frequently occur during activities of daily living. Although many studies have proposed various accident detection methods, no high-performance accident detection system is available. In this study, we propose a method for integrating data and accident detection algorithms presented in existing studies, collect new data (from two stunt performers and 15 people over age 60) using a developed wearable device, demonstrate new features and related accident detection algorithms, and analyze the performance of the proposed method against existing methods. Comparative analysis results show that the newly defined features extracted reflect more important risk factors than those used in existing studies. Further, although the traditional algorithms applied to integrated data achieved an accuracy (AC) of 79.5% and a false positive rate (FPR) of 19.4%, the proposed accident detection algorithms achieved 97.8% AC and 2.9% FPR. The high AC and low FPR for accidental falls indicate that the proposed method exhibits a considerable advancement toward developing a commercial accident detection system.

• Smart Media Journal
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• v.12 no.7
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• pp.18-26
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• 2023

In this study, we propose a cow behavior recognition system based on YOLOX and OC-SORT. YOLO X detects targets in real-time and provides information on cow location and behavior. The OC-SORT module tracks cows in the video and assigns unique IDs. The quantitative analysis module analyzes the behavior and location information of cows. Experimental results show that our system demonstrates high accuracy and precision in target detection and tracking. The average precision (AP) of YOLOX was 82.2%, the average recall (AR) was 85.5%, the number of parameters was 54.15M, and the computation was 194.16GFLOPs. OC-SORT was able to maintain high-precision real-time target tracking in complex environments and occlusion situations. By analyzing changes in cow movement and frequency of mounting behavior, our system can help more accurately discern the estrus behavior of cows.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.3
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• pp.1277-1284
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• 2016

Background: Early detection is a critical part of reducing the burden of breast cancer and breast self-examination (BSE) has been found to be an especially important early detection strategy in low and middle income countries such as Malaysia. Although reports indicate that Malaysian women report an increase in BSE activity in recent years, additional research is needed to explore factors that may help to increase this behavior among Southeastern Asian women. Objective: This study is the first of its kind to explore how the predicting variables of self-efficacy, perceived barriers, and body image factors correlate with self-reports of past BSE, and intention to conduct future breast self-exams among female students in Malaysia. Materials and Methods: Through the analysis of data collected from a prior study of female students from nine Malaysian universities (n=842), this study found that self-efficacy, perceived barriers and specific body image sub-constructs (MBSRQ-Appearance Scales) were correlated with, and at times predicted, both the likelihood of past BSE and the intention to conduct breast self-exams in the future. Results: Self-efficacy (SE) positively predicted the likelihood of past self-exam behavior, and intention to conduct future breast self-exams. Perceived barriers (BR) negatively predicted past behavior and future intention of breast self-exams. The body image sub-constructs of appearance evaluation (AE) and overweight preoccupation (OWP) predicted the likelihood of past behavior but did not predict intention for future behavior. Appearance orientation (AO) had a somewhat opposite effect: AO did not correlate with or predict past behavior but did correlate with intention to conduct breast self-exams in the future. The body image sub-constructs of body area satisfaction (BASS) and self-classified weight (SCW) showed no correlation with the subjects' past breast self-exam behavior nor with their intention to conduct breast self-exams in the future. Conclusions: Findings from this study indicate that both self-efficacy and perceived barriers to BSE are significant psychosocial factors that influence BSE behavior. These results suggest that health promotion interventions that help enhance self-efficacy and reduce perceived barriers have the potential to increase the intentions of Malaysian women to perform breast self-exams, which can promote early detection of breast cancers. Future research should evaluate targeted communication interventions for addressing self-efficacy and perceived barriers to breast self-exams with at-risk Malaysian women. and further explore the relationship between BSE and body image.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.233-235
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• 2005

This paper presents an unsupervised change detection methodology designed for the detection of landslide areas. The proposed methodology consists of two analytical steps: one for multi-temporal segmentation and the other for automatic selection of thresholding values. By considering the conditions of landslide occurrences and the spectral behavior of multi-temporal remote sensing images, some specific procedures are included in the analytical steps mentioned above. The effectiveness and applicability of the methodology proposed here were illustrated by a case study of the Gangneung area, Korea. The case study demonstrated that the proposed methodology could detect about $83\%$ of landslide occurrences.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.41-47
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• 2012

MANET has vulnerable structure on security owing to structural characteristics as follows. MANET consisted of moving nodes is that every nodes have to perform function of router. Every node has to provide reliable routing service in cooperation each other. These properties are caused by expose to various attacks. But, it is difficult that position of environment intrusion detection system is established, information is collected, and particularly attack is detected because of moving of nodes in MANET environment. It is not easy that important profile is constructed also. In this paper, conformal predictor - support vector machine(CP-SVM) based intrusion detection technique was proposed in order to do more accurate and efficient intrusion detection. In this study, IDS-agents calculate p value from collected packet and transmit to cluster head, and then other all cluster head have same value and detect abnormal behavior using the value. Cluster form of hierarchical structure was used to reduce consumption of nodes also. Effectiveness of proposed method was confirmed through experiment.

• ETRI Journal
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• v.45 no.4
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• pp.713-723
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• 2023

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.

• Journal of Korean Academy of Nursing
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• v.12 no.1
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• pp.25-38
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• 1982

As technological civilization and medical science has developed, standards of living have imp-roved and human life expectancy has been extended. But the incidence and mortality rate of cancer have been gradually increasing due to the pollution of the environment. Even though cancer is still a great threat to human beings, the etiology and appropriate cure forcancerhavenotyetbeendiscovered. The early detection and treatment of cancer is urgently needed. This study concentrates on the health behavior of woman regarding the papanicolau smear for early detection of cervical cancer. It was done in order to provide a direction for scientific health education materials by investigating predictors of preventive health behavior. The subjects for this study were made up of 54 woman, who comply with preventive health practices(compliant) who attended the Cervical Cancer Center of Y University Hospital in order to have tests for early detection of cervical cancer and 54 woman who did not comply with preventive health practices (noncompliant) selected from 100 housewives of I apartment, Kang Nam Ku, Seoul. The study method used, was a questionnaire for the compliance group and an interview for the noncompliance group. The period for data collection was from October 13th to October 24th. 1981. Analysis of the data was done using percentages, T-test, Pearson Correlation and Stepwise Multiple Regression. The results of study were as follows: 1. The hypotheses tested were based on the health belief model; 1) The first hypothesis,“The compliant may have more knowledge of the cervical cancer than the noncompliant”was rejected(T=-1.86, p>.05) 2) The second hypothesis,“The compliant may have a higher severity of cervical cancer than the noncompliant”was accepted (T=5.41, p<.001) 3) The third hypothesis, “The compliant may have a higher susceptability to cervical cancer than the noncompliant”was accepted(T=3.51, p<.01). 4) The fourth hypothesis,“The compliant may have more beneHt than cost'from the cervical cancer tests than the noncompliant" was accepted(T=7.46, p<.001). 5) The fifth hypothesis,“The compliant may have more health concern than the noncompliant”. was accepted(T=3.39, p<.01). These results show that severity, susceptability, benefit(over cost) and health concern influence the preventive health behavior in this Study. 2. In the correlation among variables, it was found that the knowledge of cervical cancer and the benefit(over cost) of preventive health behavior were negatively correlated(r=-2.75, p<.01), Severity of cervical cancer and benefit (over cost) of preventive health behavior were positively correlated(r=.280, p＜.01), severity and susceptability of cervical cancer were positively correlated(r= .238, p<.01), benefit(over cost) and health concern were positively correlated(r= .299, p<.01). The benefit(over cost) may be raised by increasing the severity and health concern. Therefore the compliance rate of woman may be raised through health education by increasing the benefit(over cost) of the individual. 3. The Stepwise Multiple Regression between health behavior and predictors. 1) The factor“Benefit(over cost)”could account for preventive health behavior in 34.4％ of the sample(F=55.6204 P<.01). 2) When the factor“Severity”is added to this, it accounts for 44.3% of preventive health behavior(F=41.679, p<.01). 3) When the factor“Susceptability”is also included, it accounts for 46.7% of preventive health behavior(F=30.373, p<.01). 4) When the factor “Health concern”is included, it accounts for 48.1% of preventive health behavior(F=23859, p<.05). This means that other factors appear to influence preventive health behavior, since the combination of variables explains only 48.1％ of the Preventive health behavior. Therefore further study to investigate the predictors of preventive health behavior is necessary.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.185-197
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• 2018

Measuring market fear is an important way of understanding fundamental economic phenomena related to financial crises. There have been several approaches to measure market fear or panic level in a financial market. Recently, herd behavior has gained its popularity as important economic phenomena explaining the fear in the financial market. In this paper, we investigate herd behavior in global stock markets with a focus on intercontinental comparison. While various risk measures are available for the detection of herd behavior in the market, we use the standardized herd behavior index in Dhaene et al. (Insurance: Mathematics and Economics, 50, 357-370, 2012b) and Lee and Ahn (Dependence Modeling, 5, 316-329, 2017) for the comparison of herd behaviors in global stock markets. A global stock market data from Morgan Stanley Capital International is used to study herd behavior especially during periods of financial crises.