• Journal of Information Management
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• v.38 no.3
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• pp.137-160
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• 2007

The purpose of this study is to examine the meaning and status of the current service of academic libraries in the aspect of its supporting roles for academic courses. The research methods include an examination of model cases from the U.S.A. and Hong Kong and also an electronic questionnaire survey of 32 academic libraries in Korea(67% response rate). With the result of the research analysis, this study aimed to provide optimal administrative plans in e-learning environment.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.1055-1062
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• 2021

As face-to-face education becomes difficult due to the spread of COVID-19, the use of e-learning content and virtual training is increasing. In the case of information security education, practice to learn response techniques is important, so simulation hacking and vulnerability analysis activities have been supported as virtual training for a long time. In order to increase the educational effect, contents should be designed similar to real situation, and learning activities to achieve the learning goals should be designed. In addition, excellent functions and scalability of the system supporting learning activities are required. The researcher developed an LMS evaluation index that supports non-face-to-face education by considering the key elements of non-face-to-face education and training. The developed evaluation index was applied to the information security education platform to verify its practical utility.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.198-201
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• 2022

Recently, new cyber threats targeting new technologies are increasing, and hackers' attack targets are becoming broader and more intelligent. To counter these attacks, major security companies are using traditional EDR (Endpoint Detection and Response) solutions. However, the conventional method does not consider the context, so there is a limit to the accuracy and efficiency of responding to an advanced attack. In order to improve this problem, the need for a security solution centered on XDR (Extended Detection and Response) has recently emerged. In this study, we present effective threat detection and countermeasures in a changing environment through XDR trends and development roadmaps using machine learning-based context analysis.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.749-752
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• 2024

연합학습은 클라이언트가 중앙 서버에 원본 데이터를 주지 않고도 학습할 수 있도록 설계된 분산된 머신러닝 방법이다. 그러나 클라이언트와 중앙 서버 사이에 모델 업데이트 정보를 공유한다는 점에서 여전히 추론 공격(Inference Attack)과 오염 공격(Poisoning Attack)의 위험에 노출되어 있다. 이러한 공격을 방어하기 위해 연합학습에 차분프라이버시(Differential Privacy)를 적용하는 방안이 연구되고 있다. 차분 프라이버시는 데이터에 노이즈를 추가하여 민감한 정보를 보호하면서도 유의미한 통계적 정보 쿼리는 공유할 수 있도록 하는 기법으로, 노이즈를 추가하는 위치에 따라 전역적 차분프라이버시(Global Differential Privacy)와 국소적 차분 프라이버시(Local Differential Privacy)로 나뉜다. 이에 본 논문에서는 차분 프라이버시를 적용한 연합학습의 최신 연구 동향을 전역적 차분 프라이버시를 적용한 방향과 국소적 차분 프라이버시를 적용한 방향으로 나누어 검토한다. 또한 이를 세분화하여 차분 프라이버시를 발전시킨 방식인 적응형 차분 프라이버시(Adaptive Differential Privacy)와 개인화된 차분 프라이버시(Personalized Differential Privacy)를 응용하여 연합학습에 적용한 방식들에 대하여 특징과 장점 및 한계점을 분석하고 향후 연구방향을 제안한다.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.65-77
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• 2022

Due to the recent development in electronic financial services, transactions of electronic prepayment are rapidly growing, leading to growing fraud attempts. This paper proposes a methodology that can effectively detect fraud transactions in electronic prepayment by machine learning algorithms, including support vector machines, decision trees, and artificial neural networks. Actual transaction data of electronic prepayment services were collected and preprocessed to extract the most relevant variables from raw data. Two different approaches were explored in the paper. One is a transaction-based approach, and the other is a user ID-based approach. For the transaction-based approach, the first model is primarily based on raw data features, while the second model uses extra features in addition to the first model. The user ID-based approach also used feature engineering to extract and transform the most relevant features. Overall, the user ID-based approach showed a better performance than the transaction-based approach, where the artificial neural networks showed the best performance. The proposed method could be used to reduce the damage caused by financial accidents by detecting and blocking fraud attempts.

• Smart Media Journal
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• v.13 no.6
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• pp.9-15
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• 2024

In recent trends, there has been an increase in 'Qshing' attacks, a hybrid form of phishing that exploits fake QR (Quick Response) codes impersonating government agencies to steal personal and financial information. Particularly, this attack method is characterized by its stealthiness, as victims can be redirected to phishing pages or led to download malicious software simply by scanning a QR code, making it difficult for them to realize they have been targeted. In this paper, we have developed a classification technique utilizing machine learning algorithms to identify the maliciousness of URLs embedded in QR codes, and we have explored ways to integrate this with existing QR code readers. To this end, we constructed a dataset from 128,587 malicious URLs and 428,102 benign URLs, extracting 35 different features such as protocol and parameters, and used AutoML to identify the optimal algorithm and hyperparameters, achieving an accuracy of approximately 87.37%. Following this, we designed the integration of the trained classification model with existing QR code readers to implement a service capable of countering Qshing attacks. In conclusion, our findings confirm that deriving an optimized algorithm for classifying malicious URLs in QR codes and integrating it with existing QR code readers presents a viable solution to combat Qshing attacks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1119-1127
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• 2018

Most of the cyber attacks are caused by malicious codes. The damage caused by cyber attacks are gradually expanded to IoT and CPS, which is not limited to cyberspace but a serious threat to real life. Accordingly, various malicious code analysis techniques have been appeared. Dynamic analysis have been widely used to easily identify the resulting malicious behavior, but are struggling with an increase in Anti-VM malware that is not working in VM environment detection. On the other hand, static analysis has difficulties in analysis due to various packing techniques. In this paper, we proposed malware classification techniques regardless of known packers or unknown packers through the proposed model. To do this, we designed a model of supervised learning and unsupervised learning for the features that can be used in the PE structure, and conducted the results verification through 98,000 samples. It is expected that accurate analysis will be possible through customized analysis technology for each class.

• Journal of Korean Home Economics Education Association
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• v.20 no.2
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• pp.1-14
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• 2008

This study was performed to evaluate the effect of the e-Learning application on the teaching learning method in Technology-Home Economics of Middle School. For e-Learning of clothing part, diverse teaching programs and contents were reconstructed and provided in cyber-space. Among the uploaded diverse materials including advanced and supplementary materials, students could make a choice which depends on their own levels. Since the results of assignments could be uploaded in cyber-space and easily accessible to all students, the reliablity of estimation was increased and self-directed learning ability was improved. Also, more effective teaching learning activity could be expected through the mutual exchange of opinion between students and teacher, and among students. Besides, counselling and advices on overall aspect of student's life were also possible. The students were generally satisfied to e-Learning instruction. The interest and understanding about clothing part were increased, and the academic attitude and habit were positively changed. Also they responded that e-Learning instruction were very helpful because learning was possible even after school, diverse materials for learning were available, and submitting the results of assignments was very convenient. In conclusion, the application of e-Learning on the teaching learning method was very effective and should be encouraged. The development and supplement of diverse programs and contents will be necessary for more successful e-Learning instructions.

• Korean Journal of Comparative Education
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• v.28 no.2
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• pp.123-151
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• 2018

The purposes of this study were to examine the current status of ICT in all ASEAN countries and to provide implications for Korea to find appropriate ways to support and collaborate with HEIs in ASEAN countries. To achieve these purposes, ASEAN countries were categorized into 3 groups based on the development stages of ICT, and the key ICT initiatives, current facts about ICT, and related issues were analyzed. The results of the study were as follows: Group 1 countries, Brunei, Malaysia, and Singapore, with relatively well-established ICT infrastructure, have established their own ICT policies and initiated e-learning programs. Group 2 countries, Indonesia, Philippines, Thailand, and Vietnam, which have relatively well-developed ICT infrastructure with existing regional gaps, showed different uses of ICT in higher education. Philippines and Thailand established their own policies based on national ICT master plans while Indonesia focused on MOOCs and Vietnam initiated cyber university projects. Group 3 countries, Cambodia, Lao PDR, and Myanmar, with the least developed ICT infrastructure in ASEAN, have also tried to develop national level strategies to utilize ICT in higher education. However, insufficient and inadequate ICT infrastructure created issues and challenges for these countries to successfully initiate ICT policies. This study suggested that it is necessary to take into serious consideration the national differences when collaborating with and supporting ASEAN countries due to the variation of ICT development stages and different levels of using ICT in higher education among ASEAN countries.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.896-905
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• 2021

In the field of SIEM(Security information and event management), many studies try to use a feedback system to solve lack of completeness of training data and false positives of new attack events that occur in the actual operation. However, the current feedback system requires too much human inputs to improve the running model and even so, those feedback from inexperienced analysts can affect the model performance negatively. Therefore, we propose "active model improving feedback technology" to solve the shortage of security analyst manpower, increasing false positive rates and degrading model performance. First, we cluster similar predicted events during the operation, calculate feedback priorities for those clusters and select and provide representative events from those highly prioritized clusters using XAI (eXplainable AI)-based event visualization. Once these events are feedbacked, we exclude less analogous events and then propagate the feedback throughout the clusters. Finally, these events are incrementally trained by an existing model. To verify the effectiveness of our proposal, we compared three distinct scenarios using PKDD2007 and CSIC2012. As a result, our proposal confirmed a 30% higher performance in all indicators compared to that of the model with no feedback and the current feedback system.