• Journal of the Korean Home Economics Association
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• v.48 no.5
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• pp.129-143
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• 2010

In order to improve the quality of student teachers' practicum, this study analysed the current situation of student teachers' field practice in child care centers. The study also assessed the practicum implementation case of H Cyber University. First, this research examined the characteristics of practicum implementation in universities. Second, a variety of strategies based on a blended model, on/off-line lectures, professor-student interaction, university-daycare centers collaboration, e-mentoring, and student teachers' portfolios that reduce the problems of student teachers' training, were explored. Finally, the practicum implementation case that adopted these factors was assessed. The results from this case study implied that student teachers' practicums based on a blended model can be an alternative method for problem-solving in existing student teachers' training in universities.

• The Journal of Society for e-Business Studies
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• v.3 no.2
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• pp.37-55
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• 1998

This study describes the works on development of a scenario-based WBT system of electronic commerce to introduce and activate electronic commerce in our society because it is mostly required to correctly understand electronic commerce and timely educate its users and technicians for entering into global electronic commerce that binds every commercial area regardless of the borders and nationalities. This system, WBT(Web - Based Training of Electronic commerce), consists of two parts. The one part provides a general concept of EC. This is developed to meet the needs of beginner for EC. The other is a simulator for cyber shopping mall. It is designed for teaching how to shop at cyber shopping mall based on various scenarios. In addition, we can simulate ourselves for EDI, which enables trading partners to conduct routine business transaction, such as purchase orders, invoices and shopping notices with it. Our commitment to the development of this application is consistent with the objectives of being easy to learn electronic commerce and its applications in real world.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.655-673
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• 2004

This paper is concerned with analysing the bankruptcy prediction power of three models: Multivariate Discriminant Analysis(MDA ), Logit Analysis, Neural Network. The after-crisis bankrupted companies were limited to the research data and the listed companies belonging to manufacturing industry was limited to the research data so as to improve prediction accuracy and validity of the model. In order to assure meaningful bankruptcy prediction, training data and testing data were not extracted within the corresponding period. The result is that prediction accuracy of neural network model is more excellent than that of logit analysis and MDA model when considering that execution of testing data was followed by execution of training data.

• International Journal of Contents
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• v.13 no.3
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• pp.32-37
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• 2017

The development of Information and Communications Technology (ICT) has changed the manner in which English teachers are taught as instructors, as well as English language learners. Online-related technology, a product of development of ICT technology, is used for various purposes such as training teachers and enhancing professionalism of current teachers to practice more efficient training. The purpose of this study is to extract common domains and sub-domains related to development of cyber-type in-service training (INSET) through domestic and international case analyses and to develop content areas of INSET using cyberspace or efficient online INSET programs. To accomplish the purpose of this study, domestic and foreign cases were analyzed in relation to direction of development of online English teacher training programs.

• Convergence Security Journal
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• v.20 no.4
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• pp.135-141
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• 2020

The Ministry of National Defense is strengthening the power and capacity of cyber operations as cyber protection training is conducted. However, considering the level of enemy cyber attack capability, the level of cyber defense capability of the ministry of national defense is significantly low and the protection measures and response system for responding to cyber threats to military networks are not clearly designed, falling short of the level of cyber security capabilities of the public and private sectors. Therefore, this paper is to investigate and verify the establishment of a military internal network vulnerability mitigation system that applies the intention of attackers, tactics, techniques and procedures information (ATT&CK Framework), identified military internal network main threat information, and military information system security requirements with military specificity as factors that can establish a defense network vulnerability mitigation system by referring to the domestic and foreign cyber security framework It has the advantage of having.

• Convergence Security Journal
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• v.22 no.1
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• pp.61-69
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• 2022

When a large-scale cyber attack or terrorism occurs and the country suffers enormous damage or poses a fatal threat to security, social interest in nurturing cybersecurity workforce increases. In addition, the government often suggests policies and guideline to train cybersecurity workforce. However, the system that can systematically manage trained cyber workforce after they are employed in related organizations or companies is still weak. Software workforce has a standardized qualification level model, so appropriate jobs are set and managed for each level. Cyber workforce also need a specialized qualification level model that takes into account their career, academic background, and education&training performance. By assigning a qualification level, the duties that can be performed for each level should be set, and the position and duty of the department should also be assigned in consideration of the level. Therefore, in this paper, we propose a qualification level model for cyber security workforce.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.65-72
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• 2020

With the development of information technology and the growth of the scale of system and network, cyber threats and crimes continue to increase. To cope with these threats, cybersecurity training based on actual attacks and defenses is required. However, cybersecurity training requires expert analysis and attack performance, which is inefficient in terms of cost and time. In this paper, we propose a cyber attack simulator that automatically executes attack techniques. This simulator generates attack scenarios by combining attack techniques modeled to be implemented and executes the attack by sequentially executing the derived scenarios. In order to verify the effectiveness of the proposed attack simulator, we experimented by setting an example attack goal and scenarios in a real environment. The attack simulator successfully performed five attack techniques to gain administrator privileges.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.387-390
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• 2017

Various education programs using cyber education in the age of knowledge informatization were introduced to schools, corporate education, military service, lifelong education facilities as well as public employee education and training system. In the case of the national government officials, the education and local civil servants are organized by 15 cities and provincial public officials training institutes, which are organized by the national civil service personnel development center cyber education center. The purpose of this study is to find out the development plan through questionnaires of the students who are in charge of cyber education in the chungcheongnam-do local civil servants.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4008-4023
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• 2022

Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.71-82
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• 2024

With the advancement of information and communication technology (ICT), the use of electronic document types such as PDF, MS Office, and HWP files has increased. Such trend has led the cyber attackers increasingly try to spread malicious documents through e-mails and messengers. To counter such attacks, AI-based methodologies have been actively employed in order to detect malicious document files. The main challenge in detecting malicious HWP(Hangul Word Processor) files is the lack of quality dataset due to its usage is limited in Korea, compared to PDF and MS-Office files that are highly being utilized worldwide. To address this limitation, data augmentation have been proposed to diversify training data by transforming existing dataset, but as the usefulness of the augmented data is not evaluated, augmented data could end up harming model's performance. In this paper, we propose an effective semi-supervised learning technique in detecting malicious HWP document files, which improves overall AI model performance via quantifying the utility of augmented data and filtering out useless training data.