• The Journal of Korean Association of Computer Education
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• v.14 no.3
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• pp.63-72
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• 2011

Satisfactory e-learning experience of working part-time adult students is a truly dynamic and multidimensional process that reflects learning needs and abilities. Special attention is given to understanding the role of student-to-faculty interaction, student-to-student interaction, e-learning content and course structure, flow, periodic off-line class meetings and synchronous Q&A sessions. Survey questions were developed and distributed to adult graduate students. Some of them were asked to complete the questions with the most interesting subjects or classes in their mind, and others with the most difficult subjects in their mind. The structural model for each group was tested. The values of path coefficients corresponding to the group with the difficult subjects turn out to be higher for the following paths; a) interaction among professors and students and satisfaction, b) contents quality and flow, c) Q&A and interaction among professors and students, d) Q&A and interaction among students. For the other paths such as interaction among students and satisfaction, contents structure and flow, the coefficient values corresponding to the group with the interesting subjects are higher. Some implications for e-learning design were provided as well.

• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1115-1121
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• 2018

In this paper, we have studied a data analysis method by deep learning to predict learning achievements based on accumulated data in cyber university learning management system. By predicting learner's academic achievement, it can be used as a tool to enhance learner's learning and improve the quality of education. In order to improve the accuracy of prediction of learning achievements, the autoencoder based attendance prediction method is developed to improve the prediction performance and deep learning algorithm with ongoing evaluation metrics and predicted attendance are used to predict the final score. In order to verify the prediction results of the proposed method, the final grade was predicted by using the evaluation factor attendance data of the learning process. The experimental result showed that we can predict the learning achievements in the middle of semester.

• The KIPS Transactions:PartA
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• v.18A no.4
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• pp.129-134
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• 2011

E-learning in Korea supports different educational needs of diverse learners. E-learning became one of the major source of educational services for schools, higher education, lifelong learning, and for special education. Many e-learning contents offered by cyber universities use HTML, CSS, and Flash, and these are known to have limitations on accessibilities. People with disabilities or aged have problems accessing such contents. The purpose of this study is to evaluate accessibility of Flash e-learning contents offered by 9 cyber universities. AccChecker is used to assess accessibility of the contents. The result shows many errors and warning with Text Equivalents, Keyboard Navigation, Properties, Depth of Tree, and Structures that restrict access. In order to improve the quality and expansion of quality e-learning contents, we need aggressive measures to obtain accessibility of contents, and these should be designed at the planning phase rather than adjusted during the development stage. Furthermore, it is vital to train instructional designers, developers and the CEOs to realize the importance of accessibility and learn appropriate skills to increase accessibilities of e-learning contents.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.99-107
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• 2022

In the current technological society, where various technologies are converged into one and being transformed into new technologies, new cyber attacks are being made just as they keep pace with the changes in society. In particular, due to the convergence of various attacks into one, it is difficult to protect the system with only the existing security system. A lot of information is being generated to respond to such cyber attacks. However, recklessly generated vulnerability information can induce confusion by providing unnecessary information to administrators. Therefore, this paper proposes a mechanism to assist in the analysis of emerging cyberattack convergence technologies by providing differentiated vulnerability information to managers by learning documents using deep learning-based language learning models, extracting vulnerability information and classifying them according to the MITRE ATT&CK framework.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.299-301
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• 2017

현대사회에서는 사이버 해킹 공격이 많이 일어나고 있다. 공격이 증가함에 따라 이를 다양한 방법으로 방어하고 탐지하는 연구가 많이 이루어지고 있다. 본 논문은 OpenIOC, STIX, MMDEF 등과 같은 공격자의 방법론 또는 증거를 식별하는 기술 특성 설명을 수집해 놓은 표현들을 기반을 머신러닝과 logstash라는 로그 수집기를 결합하는 새로운 시스템을 제안한다. 시스템은 pc에 공격이 가해졌을 때 로그 수집기를 사용하여 로그를 수집한 후에 로그의 속성 값들의 리스트를 가지고 머신러닝 알고리즘을 통해 학습시켜 분석을 진행한다. 향후에는 제안된 시스템을 실시간 처리 머신러닝 알고리즘을 사용하여 필요로그정보의 구성을 해주면 자동으로 로그정보를 수집하고 필터와 출력을 거쳐 학습을 시켜 자동 침입탐지시스템으로 발전할 수 있을 것이라 예상된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.33-34
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• 2023

성장하는 빅 데이터 시장과 빅 데이터 수의 기하급수적인 증가는 기존 컴퓨팅 환경에서 데이터 처리의 어려움을 야기한다. 특히 이미지 데이터 처리 속도는 데이터양이 많을수록 현저하게 느려진다. 이에 본 논문에서는 Apache Spark와 OpenCV를 활용한 분산 클러스터 컴퓨팅 환경의 대용량 이미지 머신러닝 시스템을 제안한다. 제안하는 시스템은 Apache Spark를 통해 분산 클러스터를 구성하며, OpenCV의 이미지 처리 알고리즘과 Spark MLlib의 머신러닝 알고리즘을 활용하여 작업을 수행한다. 제안하는 시스템을 통해 본 논문은 대용량 이미지 데이터 처리 및 머신러닝 작업 속도 향상 방법을 제시한다.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.

• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.302-305
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• 2017

사이버 공격은 점차 다양해지고, 그 위험성은 날로 심각해지고 있다. 가장 강력한 공격 중 하나는 DDoS (Distributed Denial of Service) 공격이다. 본 논문에서는 다양한 사이버 공격을 분류하고 이에 따른 방법 기법을 서술하겠다. 특히, 최신 DDoS 공격 탐지 방법을 소개하고 딥러닝 (Deep Learning)을 활용한 최신 방어 기법 연구에 대해 살펴보도록 하겠다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.201-209
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• 2009

RThe main object of this study is to stipulate the relation between e-learning characteristics and e-learner's scholastic performance through the integrated study model of perspective of educational technology and information technology. Using e-learning system quality, e-learning contents characteristics and interaction as independent variable, e-learner's scholastic performance as dependent variable and learning motivation as mediator, this study has examined the relationship among these variables. Two hundreds and twelve undergraduates in cyber university participated in the survey and filled out questionnaires related to this study. The main results are as followed. First, content's quality, technical quality and the support of school affairs have a significant effect on the e-learner's scholastic performance. Second, Learning motivation plays a partial mediating role in the relationship between e-learning characteristics and e-learner's scholastic performance. The meaningful implication of this study is that to improve e-learner's scholastic performance, we have to offer e-learners more customized various learning plans, learning contents and interaction between e-learners and e-learning systems.