• Journal of Korean Critical Care Nursing
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• v.8 no.1
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• pp.1-10
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• 2015

This study was conducted to examine the relationship among the error risk of high-alert medication, attitudes to single-person checking of medication, and medication safety activities. The participants were 60 nurses working in the intensive care unit. Data were analyzed using descriptive analysis, t-test, analysis of variance, and Pearson's correlation coefficient. The mean scores of the knowledge and certainty of high-alert medication were $0.71{\pm}0.11$ and $2.74{\pm}0.59$, respectively. The mean score of the error risk of high-alert medication was $1.63{\pm}0.24$ and that of attitudes to single checking medication was $3.32{\pm}0.49$. The error risk of high-alert medication had a positive correlation with nurses' attitudes to single checking medication (r = .258, p = .047), which is correlated with the scores for certainty of knowledge (r = .284, p = .028). Based on the results of this study, continuing education for high-alert medication and the development of an accurate protocol for single checking medication are needed to improve the stability of high-alert medication.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.1
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• pp.459-466
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• 2011

In this paper, we proposed a data mining framework for the management of alerts in order to improve the performance of the intrusion detection systems. The proposed alert data mining framework performs alert correlation analysis by using mining tasks such as axis-based association rule, axis-based frequent episodes and order-based clustering. It also provides the capability of classify false alarms in order to reduce false alarms. We also analyzed the characteristics of the proposed system through the implementation and evaluation of the proposed system. The proposed alert data mining framework performs not only the alert correlation analysis but also the false alarm classification. The alert data mining framework can find out the unknown patterns of the alerts. It also can be applied to predict attacks in progress and to understand logical steps and strategies behind series of attacks using sequences of clusters and to classify false alerts from intrusion detection system. The final rules that were generated by alert data mining framework can be used to the real time response of the intrusion detection system.

• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.199-208
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• 2004

In this paper, we propose and implement SIA System for filtering redundant alert messages and dividing them into four statuses. Also, we confirm that our system can find and analyze vulnerability types of network intrusion by attackers in a managed network, so that it provides very effective means for security managers to cope with security threats in real time.

• Journal of Microbiology
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• v.45 no.2
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• pp.98-104
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• 2007

Chlorophyll ${\alpha}$ concentration and cyanobacterial cell density are regularly employed as dual criteria for determinations of the alert level for cyanobacterial bloom. However, chlorophyll ${\alpha}$ is not confined only to the cyanobacteria, but is found universally in eukaryotic algae. Furthermore, the determination of cyanobacterial cell counts is notoriously difficult, and is unduly dependent on individual variation and trained skill. A cyanobacteria-specific parameter other than the cell count or chlorophyll ${\alpha}$ concentration is, accordingly, required in order to improve the present cyanobacterial bloom alert system. Phycocyanin has been shown to exhibit a strong correlation with a variety of bloom-related factors. This may allow for the current alert system criteria to be replaced by a three-stage alert system based on phycocyanin concentrations of 0.1, 30, and $700\;{\mu}g/L$. This would also be advantageous in that it would become far more simple to conduct measurements without the need for expensive equipment, thereby enabling the monitoring of entire lakes more precisely and frequently. Thus, an alert system with superior predictive ability based on highthroughput phycocyanin measurements appears feasible.

• Journal of Korea Game Society
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• v.14 no.5
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• pp.61-68
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• 2014

In the generalization of mobile content use, feedback is a kind of alert that affects content immersion. An alert leads to separation from the content currently being used to transfer to content that raises the alert. Immersive interference can be recognized as a problem in mobile contents use. In this paper, we propose a serious game for overcomes immersion. interference from feedback and the foundation for interrelation research between feedback and immersion. The proposed serious game has been designed to present three kinds of feedback, specifically positive, negative, and hybrid feedback, through social information about the user. We also conducted an experiment to examine the correlation between three kinds of feedback and immersion while consuming digital content. The result of the experiment showed that negative feedback leads to higher immersion than positive feedback.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.665-674
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• 2003

In this paper, we propose an approach to correlate alerts using a clustering analysis of data mining techniques in order to support intrusion detection system. Intrusion detection techniques are still far from perfect. Current intrusion detection systems cannot fully detect novel attacks. However, intrucsion detection techniques are still far from perfect. Current intrusion detection systems cannot fully detect novel attacks or variations of known attacks without generating a large amount of false alerts. In addition, all the current intrusion detection systems focus on low-level attacks or anomalies. Consequently, the intrusion detection systems to underatand the intrusion behind the alerts and take appropriate actions. The clustering analysis groups data objects into clusters such that objects belonging to the same cluster are similar, while those belonging to different ones are dissimilar. As using clustering technique, we can analyze alert data efficiently and extract high-level knowledgy about attacks. Namely, it is possible to classify new type of alert as well as existed. And it helps to understand logical steps and strategies behind series of attacks using sequences of clusters, and can potentially be applied to predict attacks in progress.

• Journal of KIISE:Information Networking
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• v.31 no.5
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• pp.438-449
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• 2004

With the growing deployment of network and internet, the importance of security is also increased. But, recent intrusion detection systems which have an important position in security countermeasure can't provide proper analysis and effective defence mechanism. Instead, they have overwhelmed human operator by large volume of intrusion detection alerts. In this paper, we propose an efficient alert analysis system that can produce high level information by analyzing and processing the large volume of alerts and can detect large-scale attacks such as DDoS in early stage. And we have measured processing rate of each elementary module and carried out a scenario-based test in order to analyzing efficiency of our proposed system.

• Journal of the Korea Organic Resources Recycling Association
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• v.21 no.3
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• pp.93-98
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• 2013

Main causes of algal bloom was studied in Paldang reservoir. Statistical approach was tried using meteorological and water quality data. Algae alert system showed that more than ten days were counted in a year, once it happened in Paldang reservoir. Alert dates increased in recent 5 years. Correlation coefficients between chlorophyll-a and other indexes did not showed strong relations resulting in coefficients less than 0.4. Among them, sunshine duration, BOD, and flow rate were appeared relatively main causes of algal blooming. Sunshine duration and BOD showed positive relation while flow rate did negative one, which is resonable for photosynthetic microorganisms. Water temperature and total phosphorus, which were presumed probable main causes before study, resulted in low correlation coefficients. Correlation coefficients between discharge flow and rainfall, water temperature showed positive relation due to seasonal effect.

• Journal of Korean Critical Care Nursing
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• v.15 no.2
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• pp.1-13
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• 2022

Purpose : The study aimed to identify relationship among intensive care unit (ICU) nurses' critical thinking disposition, medication error risk level of high-alert medication, and medication safety competency, as well as the factors affecting medication safety competency. Methods : The participants were 266 ICU nurses of one higher-tier general hospital and one general hospital in Province. The data were collected using structured self-administered questionnaire from August 10 to August 31, 2021. Measurements included the critical thinking disposition questionnaire, nurses's knowledge of high-alert medication questionnaire, the medication safety competency scale. Data were analyzed using hierarchical multiple regressions using SPSS/WIN 28.0. Results : In the multiple regression analysis, the medication safety competence has a statistically significant correlation with the working department, the critical thinking disposition, and medication error risk level of high-alert medication. Conclusion : Based on the results of this study, it is suggested to develop and apply an educational strategy that can strengthen the knowledge and skills of critical thinking disposition and medication error risk level of high-alert medication to improve the ICU nurse's medication safety competency.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.39-49
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• 2014

Cyber threats on the Internet are tremendously increasing and their techniques are also evolving constantly. Intrusion Detection System (IDS) is one of the powerful solutions for detecting and analyzing the cyber attacks in realtime. Most organizations deploy it into their networks and operate it for security monitoring and response service. However, IDS has a fatal problem in that it raises a large number of alerts and most of them are false positives. In order to cope with this problem, many approaches have been proposed for the purpose of automatically identifying whether the IDS alerts are caused by real attacks or not. In this paper, we present an alert verification method based on correlation analysis between vulnerability inspection results for real systems that should be protected and the IDS alerts. In addition, we carry out practical experiments to demonstrate the effectiveness of the proposed verification method using two types of real data, i.e., the IDS alerts and the vulnerability inspection results.