• The KIPS Transactions:PartC
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• v.18C no.6
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• pp.369-378
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• 2011

To ensure data confidentiality and fine-grained access control in business environment, system model using KP-ABE(Key Policy-Attribute Based Encryption) and PRE(Proxy Re-Encryption) has been proposed recently. However, in previous study, data confidentiality has been effected by decryption right concentrated on cloud server. Also, Yu's work does not consider a access privilege management, so existing work become dangerous to collusion attack between malicious user and cloud server. To resolve this problem, we propose secure system model against collusion attack through dividing data file into header which is sent to privilege manager group and body which is sent to cloud server. And we construct the model of access privilege management using AONT based XOR threshold Secret Sharing, In addition, our scheme enable to grant weight for access privilege using XOR Share. In chapter 4, we differentiate existing scheme and proposed scheme.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.187-194
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• 2019

Ever since the world's largest Bitcoin Echange, (Mt. Gox), was closed in March 2014 due to the series of hacking, still many other Exchages incl. recent Coinale in Korea have been attacked. Those hacking attempts never stopped and have caused significant threats to the overall industry of Crypto Currency and resulted in the loss of individual investors' asset. The DEX (Decentralized Exchange) has been proposed as a solution to fix the security problem at the Exchange, but still it is far away to resolve all issues. Therefore, this paper firstly analyzes security threats against existing Crypto Currency Exchanges and secondly derives security requirements for them. To do that it proposes a secure and distributed Crypto Currency Transaction Model through Personal Security devices as a solution. The paper also proves this new attempt by demonstrating its unique modelling; ultimately by adopting this modeling into Crypto Exchange is to avoid potential security threats.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.9-16
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• 2019

In this paper, it will analyze security problems, so technology's potential can apply to business security area. First, in order to deep learning do security tasks sufficiently in the business area, deep learning requires repetitive learning with large amounts of data. In this paper, to acquire learning ability to do stable business tasks, it must detect abnormal IP packets and attack such as normal software with malicious code. Therefore, this paper will analyze whether deep learning has the cognitive ability to detect various attack. In this paper, to deep learning to reach the system and reliably execute the business model which has problem, this paper will develop deep learning technology which is equipped with security engine to analyze new IP about Session and do log analysis and solve the problem of mathematical role which can extract abnormal data and distinguish infringement of system data. Then it will apply to business model to drop the vulnerability and improve the business performance.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.93-102
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• 2019

This paper aims to set the priorities for the cybersecurity strategy of Cambodian government. To this end, we built a AHP research model by adopting 4 factors from the ITU national interests model and selecting 7 strategies from best practices of 8 countries leading the cyber security. Using a questionnaire, 19 experts evaluated Cambodia's cybersecurity strategy priorities. The key policy factors were evaluated in the order of homeland defense, economic welfare, value promotion and favorable world order. Their strategic alternatives were identified in the order of legislation, capacity building, and cyber attack prevention for critical infrastructure. This study will contribute to setting the strategic priorities and feasible action plans to strengthen Cambodia's cybersecurity capabilities.

• Knowledge Management Research
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• v.22 no.4
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• pp.41-70
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• 2021

The use of social media has many advantages such as knowledge sharing, social networking, and communicating with other people. However, it has given rise to various side effects including stress, Which is defined as social network stress in this study. This study aims to conceptualize social network stress and investigate its effect on switching behavior in social media. For this purpose, we present a research model that consists of the antecedents and consequences of social network stress and test it empirically using LISREL 8.7 based on the structural equation model. The empirical results showed that knowledge sharing and self-disclosure had positive impact on social network stress, which in turn positively influenced social media switching behaviors. In conclusion, we discussed both theoretical and practical implications of this research and suggested its limitations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1898-1903
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• 2021

Recently, the need to be wary of internal access such as internal access as well as external attackers' access to work has increased due to network expansion, cloud infrastructure expansion, and changes in working patterns due to COVID-19 situations. For this reason, a new network security model called Zero Trust is drawing attention. Zero Trust has a key principle that a trusted network does not exist, and in order to be allowed access, it must be authenticated first, and data resources can only be accessed by authenticated users and authenticated devices. In this paper, we will explain these zero trust and zero trust architectures and examine new security application strategies applicable to various companies using zero trust and the process of building a new security system based on the zero trust architecture model.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.23-31
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• 2022

Recently, the importance of the role of the university information security department in nurturing security experts to defend against cyber attacks is increasing day by day. The current university security curriculum has a problem in that the proportion of theoretical education is high and the professionalism of practical education is relatively low. This study analyzed the recent educational programs of domestic and foreign security education institutions for the purpose of improving the practical ability of the Department of Security, designing a class model suitable for the core specialization process, and suggesting the direction. The proposed model improves the existing problems of basic class connection and security practice curriculum roadmap, and additionally explains the practice program of the five core specialized subjects. This study intends to contribute to the improvement of the quality of the curriculum and educational model of each university's security department.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.113-120
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• 2024

The population sparseness index(PSI) is being utilized to describe the functioning of internal layers in artificial neural networks from the perspective of neurons, shedding light on the black-box nature of the network's internal operations. There is research indicating a positive correlation between the PSI and performance in each layer of convolutional neural network models for image classification. In this study, we observed the internal operations of a convolutional neural network when adversarial examples were applied. The results of the experiments revealed a similar pattern of positive correlation for adversarial examples, which were modified to maintain 5% accuracy compared to applying benign data. Thus, while there may be differences in each adversarial attack, the observed PSI for adversarial examples demonstrated consistent positive correlations with benign data across layers.

• Smart Media Journal
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• v.13 no.6
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• pp.24-34
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• 2024

If the future can be predicted from network traffic data, which is a time series, it can achieve effects such as efficient resource allocation, prevention of malicious attacks, and energy saving. Many models based on statistical and deep learning techniques have been proposed, and most of these studies have focused on improving model structures and learning algorithms. Another approach to improving the prediction performance of the model is to obtain a good-quality data. With the aim of obtaining a good-quality data, this paper applies a dense sampling technique that augments time series data to the application of network traffic prediction and analyzes the performance improvement. As a dataset, UNSW-NB15, which is widely used for network traffic analysis, is used. Performance is analyzed using RMSE, MAE, and MAPE. To increase the objectivity of performance measurement, experiment is performed independently 10 times and the performance of existing sparse sampling and dense sampling is compared as a box plot. As a result of comparing the performance by changing the window size and the horizon factor, dense sampling consistently showed a better performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.617-629
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• 2020

In order to overcome the limitations of the rule-based intrusion detection system due to changes in Internet computing environments, the emergence of new services, and creativity of attackers, network anomaly detection (NAD) using machine learning and deep learning technologies has received much attention. Most of these existing machine learning and deep learning technologies for NAD use supervised learning methods to learn a set of training data set labeled 'normal' and 'attack'. This paper presents the feasibility of the unsupervised learning AutoEncoder(AE) to NAD from data sets collecting of secured network traffic without labeled responses. To verify the performance of the proposed AE mode, we present the experimental results in terms of accuracy, precision, recall, f1-score, and ROC AUC value on the NSL-KDD training and test data sets. In particular, we model a reference AE through the deep analysis of diverse AEs varying hyper-parameters such as the number of layers as well as considering the regularization and denoising effects. The reference model shows the f1-scores 90.4% and 89% of binary classification on the KDDTest+ and KDDTest-21 test data sets based on the threshold of the 82-th percentile of the AE reconstruction error of the training data set.