• Knowledge Management Research
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• v.9 no.4
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• pp.1-16
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• 2008

The purpose of the present study is to explain the effects of academic self-efficacy, self-regulated learning and online task value on academic achievement and learning transfer in corporate cyber education. 202 students who completed S corporate's cyber courses in 2007 and responded to all survey participated in this study. A hypothetical model was proposed, which was composed of academic self-efficacy, online task value and self-regulated learning factors as prediction variables, and learning transfer as well as academic achievement factors as outcome variables. The results of this study through regression analysis as follows. First, learners' academic self-efficacy, self-regulated learning and online task value predict learners' academic achievement significantly. Second, except for academic self-efficacy, learners' self-regulated learning and online task value predict on learners' learning transfer significantly. Third, academic achievement plays a role as mediating value in predicting academic achievement by online task. It implies that learners' academic self-efficacy, online task value and self-regulated learning which predict learners' academic achievement and learning transfer should be considered in developing strategies for the design and operation of cyber courses.

• Management & Information Systems Review
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• v.14
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• pp.79-99
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• 2004

Cyber education system service is in the field of software service which is highlighted after the latter half of 1990'. But the progress of this service is impeded by the lack of back office which contributes to the evaluation of learning achievement and the management of learning progress. This article points out the problem of current back office which is the most important in the cyber education system, and focuses on the new intelligent learning achievement evaluation module. First, we define the cause and effect between the learning stages using by fuzzy implication which is the important part of fuzzy theory. Next, we suggest the model which generates the results of the learning achievement evaluation. This model, suggested by this article, may contribute to the development of the cyber education system by improving the current on-line education service.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.3
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• pp.42-50
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• 2003

E-Learning is a modern main teaching method starting from the concept of remote education. This research is aimed for proposing cyber lecture library system and designing a cyber lecture component that becomes a basis for e-Learning system Cyber lecture library is a storage system of cyber lectures that can supply high quality data to the needed developers. Component consists of 5 categories and those are text voice, image, animation, and flash. By using this system the developers can save the necessary time and effort in education development. This system also helps students. The students can access various lecture data on a given subject and select the best fit for them.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.443-454
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• 2020

The purpose of this study is to propose a structural theoretical model that can explain the learning flow of online distance education participants and to clarify the structural causal relationship among related variables that affect the learning flow of cyber university learners. Study results found a modified final model with a statistically significant fit based on the structural research model proposed in this study explained the learning flow of cyber university learners. Second, as a result of verifying the relationship between learning motivation and learning flow of cyber university learners, it was found that intrinsic motivation had a significant positive effect on learning flow. Third, as a result of the empirical verification on the relationship between academic self-efficacy and learning flow of cyber university learners, it was found that academic self-efficacy had a significant effect on learning flow. Fourth, as a result of empirical verification on the relationship between self-directed learning and the learning flow of cyber university learners, it was found that the learner's self-directed learning ability had a significant effect on learning flow. Such research results can be used to suggest a strategic direction for successful settlement and diffusion of a new paradigm of online non-face-to-face distance education, which has recently attracted attention.

• Journal of Nutrition and Health
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• v.35 no.8
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• pp.886-895
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• 2002

Introduction and application of virtual education has been rapidly increased in these days. A variation of information communication technology has an effect on education in interconnect with network as internet in the world that exceed the limit of time and regional. Computer and network communication technology through the medium of internet make an entrance cyber education as a new education paradigm. It must be affective on learner who have various educational characteristics and requirements. It begins to appear quality, quantity improvements of knowledge and the development of information technology that web based cyber education. This study was conducted to develop the web based education program and to evaluate the effectiveness of learning satisfaction and accomplishment and to compare the cyber lecture system with the traditional lecture system During the second semester of 2001, this study was investigated 317 registered students in a "Food and Culture" class at Kyungwon University. The data were obtained from pre and post-study with self-administered questionnaire. The evaluation and satisfaction score of students who were registered in cyberclass was negative tendency to compare pre with post-test scores, because of insuffciency of computer-aided lecture system. The major problem was inconvenient in checking system for connecting times in cyberclass which was one of evaluation point in final score. Another problem was frequently disconnection during cyber studying and not to concentrate each time in the cyber lecture because of eye fatigue, boring due to less interesting contents than other newly developed web-site. The students was prefer to mix type of the cyber and traditional lecture type class. The result of final score an each class, the score of cyber class (71.36 $\pm$ 22.44) was significantly lower than other groups (mixed type : 76.66 $\pm$ 19.99, traditional type :79.17 $\pm$ 15.72) (p < 0.05). Cyber class was attempted to present a useful and interesting teaching and learning tool which can be applied successfully in a longer term. The result suggest that various teaching and learning strategies should be developed considering the fact that the student learn alone most in time.t in time.

• Journal of Gifted/Talented Education
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• v.19 no.1
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• pp.119-140
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• 2009

This paper examined its possibility and made its new definition by finding relevant bases in order to make a close inquiry into its Identity and direction at this point when cyber-based gifted education academy is established and operated again by its necessity And 4 models which can be used in special education for the gifted were developed making a link with special education for the gifted by collecting and re-classifying cyber educational methods developed by basic research as priority of the educational method which is considered to be the most urgent issue in practical cyber learning. It is a project-type cooperation education model, an information collection-type research education model, a community-type discussion education model, and a problem focus-type e-PBL education model. To apply developed leaching-learning models to reality, students at gifted education academy in Gyeonggi Cyber Gifted Province were imputed models in different ways respectively for 4 months. As a result of analysis and statistical data of activity level and satisfaction level of students who participated in learning activity, it appeared that high level of satisfaction and active activity level were induced compared to the previous method based on tasks. It is expected that this paper will provide the bases when each cyber-based gifted education academy plans operation plan later on, and it will provide proper methods when cyber guidance teachers plan class activities.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• Proceedings of the Korea Society of Design Studies Conference
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• 2002.11b
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• pp.28-29
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• 2002

• ETRI Journal
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• v.42 no.2
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• pp.292-304
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• 2020

As side-channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side-channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning-based side-channel analysis. In particular, we outline how deep learning is applied to side-channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.