• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.27-37
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• 2022

Virtual private network (VPN) services are used in various environments related to national security, such as defense companies and defense-related institutions where digital communication environment technologies are diversified and access to network use is increasing. However, the number of cyber attacks that target vulnerable points of the VPN has annually increased through technological advancement. Thus, this study identified security requirements by performing STRIDE threat modeling to prevent potential and new vulnerable points that can occur in the VPN. STRIDE threat modeling classifies threats into six categories to systematically identify threats. To apply the proposed security requirements, this study analyzed functions of the VPN and formed a data flow diagram in the VPN service process. Then, it collected threats that can take place in the VPN and analyzed the STRIDE threat model based on data of the collected threats. The data flow diagram in the VPN service process, which was established by this study, included 96 STRIDE threats. This study formed a threat scenario to analyze attack routes of the classified threats and derived 30 security requirements for each element of the VPN based on the formed scenario. This study has significance in that it presented a security guideline for enhancing security stability of the VPN used in facilities that require high-level security, such as the Ministry of National Defense (MND).

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.643-662
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• 2023

Purpose: The purpose of this study was to understand an individuals' COVID-19 vaccine acceptance intention during the peak of the pandemic by utilizing the coping theory and technology threat avoidance theory (TTAT) as a framework. Specifically, we focused on understanding how inward and outward emotion-focused coping (EFC), such as psychological distancing and emotional support seeking, affect problem-focused behavior (PFC), which is vaccine acceptance. Furthermore, we investigate how the individuals' cognitive appraisal to- ward COVID-19, consisted of perceived threat and perceived avoidability act as an antecedent of EFC. Methods: A PLS-SEM analysis was conducted to find the causal relation between the variables. An online survey was conducted targeting vaccination recipients on April, 2021. Participants were asked about their perception toward the virus, their coping strategy, and vaccine acceptance intention. A total of 186 valid samples were collected and used for the analysis. Furthermore, to analyze the out-of-sample predictive power of the research model and ensure the generalizability of the results, a PLSpredict analysis was conducted. Results: The results of the PLS-SEM analysis show that perceived threat toward COVID-19 significantly affect an individuals' EFC strategy. Furthermore, both types of inward EFC (psychological distancing, wishful thinking) negatively affected vaccine acceptance intention. On the other hand, emotional support seeking, which is a type of outward EFC, positively affected vaccine acceptance. The result of the PLSpredict analysis confirms the generalizability of the PLS-SEM result. Conclusion: The results of our study could be utilized to decrease vaccine hesitancy and prevent global pandemics by accelerating and increasing vaccination. Our study provides several meaningful implications to researchers and practitioners regarding vaccine acceptance and threat coping behavior.

• The Bulletin of The Korean Astronomical Society
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• v.34 no.1
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• pp.132.1-132.1
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• 2009

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.61-74
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• 2004

Risk analysis model is systematic and structural process that considers internal security problems and threat factors of the information systems to find optimal level of security control. But, the risk analysis model is just only defined conceptually and there are not so many empirical studies. This research used structural equation modeling(SEM) research methodology with rigorously validated research instrument. Based on results of this study, risk analysis methodology was proved to be practically useful in e-commerce environment. Factors like threat and control were significantly related to risk. In conclusion, the results of this study can be applied to general situation or environment of information security for analyzing and managing the risk and providing new approach to comprehend concept of risk in e-commerce environment.

• Journal of the Korea Society of Computer and Information
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• v.28 no.7
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• pp.47-55
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• 2023

This paper presents a method for predicting the threat index of combat systems using Gradient Boosting Regressors and Support Vector Regressors among machine learning models. Currently, combat systems are software that emphasizes safety and reliability, so the application of AI technology that is not guaranteed to be reliable is restricted by policy, and as a result, the electrified domestic combat systems are not equipped with AI technology. However, in order to respond to the policy direction of the Ministry of National Defense, which aims to electrify AI, we conducted a study to secure the basic technology required for the application of machine learning in combat systems. After collecting the data required for threat index evaluation, the study determined the prediction accuracy of the trained model by processing and refining the data, selecting the machine learning model, and selecting the optimal hyper-parameters. As a result, the model score for the test data was over 99 points, confirming the applicability of machine learning models to combat systems.

• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.301-315
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• 2022

Purpose - The main objective of this study was to investigate tourists behavior by applying protection motivation theory and health belief model during COVID-19 pandemic. Specifically, the study examined how risk perception of COVID-19 affects tourists' protection motivation and travel avoidance intention. Design/methodology/approach - The empirical data was collected by self-administered questionnaires to obtain perception and behavior regarding COVID-19 pandemic situation. A total of 486 questionnaires were used for data analysis and SEM analysis was applied in order to examine seven hypotheses. Findings-The results showed that COVID-19 risk perception is a significant antecedent of threat appraisal, coping appraisal, and cue to action (H1, H2, H6). Moreover, protection motivation is affected by threat appraisal and coping appraisal (H3, H4) and influences on travel avoidance intention (H5). However, cue to action does not affect protection motivation (H7). Research implications or Originality - This study provides insightful implications for tourism industry practitioners who will prepare the post-corona field and the results enrich knowledge of the tourist behavior during pandemic situation.

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.202-208
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• 2024

Objective: The purpose of this study is to examine the competitive environment of 7-Eleven using Porter's 5 Force model to analyze the specific competitiveness of 7-Eleven that has maintained continuous growth as a competitive company. Methods: This study is a case study of 7-Eleven. Results: As a result of the analysis, 7-Eleven has reinforced its competitiveness with existing competitors by raising entry barriers by expanding domestic stores and developing overseas convenience store business, focusing on North America, based on economy of scale. In addition, 7-Eleven seeks to differentiate itself from other convenience stores by developing a "private brand" and strengthens its bargaining power with suppliers through the development of new products by gathering information and know-how of experts in product development based on customer needs. The bargaining power with customers has been strengthened by building loyalty and trust in the brand by allowing consumers to purchase the same products at the same price no matter which store they visit. As a threat to potential competitors, 7-Eleven has secured a competition advantage by raising the barrier to entry by concentrating stores in specific areas through a dominant franchise development strategy and increasing awareness of 7-Eleven among consumers. In the case of threats from substitute products, it was confirmed that 7-Eleven has overcome the threat from substitute products by opening 24 hours a day and providing various services such as ATMs and copiers.

• Journal of IKEEE
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• v.22 no.1
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• pp.162-167
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• 2018

In this paper, we propose a method to classify radar signals according to the jamming technique by applying the machine learning to parameter data extracted from received radar signals. In the present army, the radar signal is classified according to the type of threat based on the library of the radar signal parameters mostly built by the preliminary investigation. However, since radar technology is continuously evolving and diversifying, it can not properly classify signals when applying this method to new threats or threat types that do not exist in existing libraries, thus limiting the choice of appropriate jamming techniques. Therefore, it is necessary to classify the signals so that the optimal jamming technique can be selected using only the parameter data of the radar signal that is different from the method using the existing threat library. In this study, we propose a method based on machine learning to cope with new threat signal form. The method classifies the signal corresponding the new jamming method for the new threat signal by learning the classifier composed of the hidden Markov model and the neural network using the existing library data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.595-602
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• 2021

This study evaluates the impact of an information security policy sanction, a perceived threat, and the perception of the information security climate on a compliance behavioral intention. The research method was structured with a cross-sectional study design for the prospect and goal orientation. The variables used in the analysis are information security policy sanction, perceived threat, perception of information security climate, and compliance behavioral intention. Progress in this research consists of measuring the prospect and goal orientation, and then measuring the four variables. As a result, the prospect had a significant effect on the perception of the information security climate, and it was found that the influence of the gain-based condition was greater than the loss-based condition. Goal orientation had a significant effect on the information security policy sanction, the perceived threat, and the compliance behavioral intention, and the influence of the development-based condition was greater than the stability-based condition. Both prospect and goal orientation had an interactive effect on the compliance behavioral intention. The exploration model was verified as a mediation model. In addition, the discussion includes the appropriate implications for information security based on these research results.