• Journal of Korean Academy of Nursing
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• v.44 no.5
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• pp.525-533
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• 2014

Purpose: This study was done to propose and test a predictive model that would explain and predict fall prevention behaviors in postmenopausal women. The health belief model was the theoretical basis to aid development of a nursing intervention fall prevention program. Methods: Data for 421 postmenopausal women were selected from an original data set using a survey design. The structural equation model was tested for 3 constructs: modifying factors, expectation factors, and threat factors. Expectation factors were measured as relative perceived benefit (perceived benefit minus perceived barrier), self-efficacy, and health motivation; threat factors, as perceived susceptibility (fear of falling) and perceived severity (avoiding activity for fear of falling); and modifying factors: level of education and knowledge about fall prevention. Data were analyzed using SPSS Windows and AMOS program. Results: Mean age was 55.7 years (range 45-64), and 19.7% had experienced a fall within the past year. Fall prevention behaviors were explained by expectation and threat factors indicating significant direct effects. Mediating effect of health beliefs was significant in the relationship between modifying factors and fall prevention behaviors. The proposed model explained 33% of the variance. Conclusion: Results indicate that fall prevention education should include knowledge, expectation, and threat factors based on health belief model.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.125-139
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• 2017

In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1722-1737
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• 2019

We developed an insider threat detection model to be used by organizations that repeat tasks at regular intervals. The model identifies the best combination of different feature selection algorithms, unsupervised learning algorithms, and standard scores. We derive a model specifically optimized for the organization by evaluating each combination in terms of accuracy, AUC (Area Under the Curve), and TPR (True Positive Rate). In order to validate this model, a four-year log was applied to the system handling sensitive information from public institutions. In the research target system, the user log was analyzed monthly based on the fact that the business process is processed at a cycle of one year, and the roles are determined for each person in charge. In order to classify the behavior of a user as abnormal, the standard scores of each organization were calculated and classified as abnormal when they exceeded certain thresholds. Using this method, we proposed an optimized model for the organization and verified it.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.2
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• pp.76-81
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• 2017

In this paper, the risk analysis of smart home services was implemented by applying threat modeling. Identified possible threats for safe deployment of smart home services and identified threats through the STRIDE model. Through the creation of the Attack Tree, the attackable risk was analyzed and the risk was measured by applying the DREAD model. The derived results can be used to protect assets and mitigate risk by preventing security vulnerabilities from compromising and identifying threats from adversely affecting services. In addition, the modeled result of the derived threat can be utilized as a basis for performing the security check of the smart home service.

• The Journal of Information Systems
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• v.26 no.3
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• pp.313-337
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• 2017

Purpose Financial technology, also known as FinTech, is conceptually defined as a new type of financial service which is combined with information technology and other traditional financial services like payments, investments, financing, insurance, asset management and so on. Most of the studies on FinTech services have been conducted from the viewpoint of technical issues or legal and institutional studies, and few studies are conducted from the health belief perspectives and security behavior approaches. In this regard, this study suggest an extended information protection behavior model. Design/Methodology/Approach The Health Belief Model (HBM), the Protection Motivation Theory (PMT), and the Technology Threat Avoidance Theory (TTAT) were employed to identify constructs relevant to information protection behavior of FinTech services. A new extended information protection behavior model in which the influence factors of information protection behavior (i.e., perceived susceptibility, perceived severity, perceived benefits, perceived barriers, perceived self-efficacy, subjective norms) affect perceived threats and perceived responsiveness positively, leading to information protection behavior of FinTech users eventually. This study developed an extended information protection behavior model to explain the protection behavior intention in FinTech users and collected 272 survey responses from the mobile users who had experiences with such mobile payments and FinTech services. Findings The finding of this study suggests that the influence factors of information protection behavior affect perceived threats and perceived responsiveness positively, and information protection behavior of FinTech users as well.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.9
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• pp.689-699
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• 2013

During extreme ionospheric storms, anomalous ionospheric delays and gradients could cause potential integrity threats to users of GNSS (Global Navigation Satellite System) augmentation systems. GNSS augmentation ground facilities must monitor these ionospheric anomalies defined by a threat model and alarm the users of safely-of-life applications within time-to-alerts. Because the ionospheric anomaly threat model is developed using data collected from GNSS reference stations, the use of poor-quality data can degrade the performance of the threat model. As the total number of stations increases, the number of station with poor GNSS data quality also increases. This paper analyzes the quality of data collected from Korean GPS reference stations using comprehensive GNSS data quality check algorithms. The results show that the range of good and poor qualities varies noticeably for each quality parameter. Especially erroneous ionospheric delay and gradients estimates are produced due to poor quality data. The results obtained in this study should be a basis for determining GPS data quality criteria in the development of ionospheric threat models.

• International Journal of Contents
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• v.15 no.1
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• pp.32-38
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• 2019

In an environment where a large number of weapons are operated compared to a large number of ground targets, it is important to monitor and manage the targets to set up a fire plan, and through their multilateral analysis, to equip them with a priority order process for targets having a high threat level through the quantitative calculation of the threat level. Existing studies consider the anti-aircraft and anti-ship targets only, hence, it is impossible to apply the existing algorithm to ground weapon system development. Therefore, we proposed an effective threat evaluation algorithm for multiple ground targets in multi-target and multi-weapon environments. Our algorithm optimizes to multiple ground targets by use of unique ground target features such as proximity degree, sorts of weapons and protected assets, target types, relative importance of the weapons and protected assets, etc. Therefore, it is possible to maximize an engagement effect by deducing an effective threat evaluation model by considering the characteristics of ground targets comprehensively. We carried out performance evaluation and verification through simulations and visualizations, and confirmed high utility and effect of our algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.43-52
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• 2021

In weapon assignment studies to defend against threats such as ballistic missiles and long range artillery, threat assessment was partially lacking in analysis of various threat attributes, and considering the threat characteristics of warheads, which are difficult to judge in the early flight stages, it is very important to apply more reliable optimal solutions than approximate solution using LP model, Meta heuristics Genetic Algorithm, Tabu search and Particle swarm optimization etc. Our studies suggest Generic Rule based threat evaluation and weapon assignment algorithm in the basis of various attributes of threats. First job of studies analyzes information on Various attributes such as the type of target, Flight trajectory and flight time, range and intercept altitude of the intercept system, etc. Second job of studies propose Rule based threat evaluation and weapon assignment algorithm were applied to obtain a more reliable solution by reflection the importance of the interception system. It analyzes ballistic missiles and long-range artillery was assigned to multiple intercept system by real time threat assessment reflecting various threat information. The results of this study are provided reliable solution for Weapon Assignment problem as well as considered to be applicable to establishing a missile and long range artillery defense system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.6
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• pp.544-550
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• 2009

To analyze the threat decision performance of MWR(Missile Warning Radar) give analysis on condition that we decide the effective threat using the POC(Probability of Over Countermeasure)/PUC(Probability of Under Countermeasure). Thus, we execute the simulation using the Monte-Carlo method to analyze effect, but the execution time of simulation took longer than we expected. In this paper, the effect analysis is proposed using the probability model to reduce the execution time of simulation. We present the setting method of parameter for probability model and the effect analysis result of MWR using the simulation. Also, we present the comparison result of simulation execution time for Monte-Carlo and probability model.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.231-237
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• 2017

Threat intelligences collected from cyber incident sharing system and security events collected from Security Information & Event Management system are analyzed and coped with expanding malicious code rapidly with the advent of big data. Analytical classification of the threat intelligence in cyber incidents requires various features of cyber observable. Therefore it is necessary to improve classification accuracy of the similarity by using multi-profile which is classified as the same features of cyber observables. We propose a multi-profile ensemble model performed similarity analysis on cyber incident of threat intelligence based on both attack types and cyber observables that can enhance the accuracy of the classification. We see a potential improvement of the cyber incident analysis system, which enhance the accuracy of the classification. Implementation of our suggested technique in a computer network offers the ability to classify and detect similar cyber incident of those not detected by other mechanisms.