• Journal of Information Technology Services
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• v.12 no.4
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• pp.1-23
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• 2013

This study examines whether key characteristics of cloud computing services would affect the intention of use for personalized cloud computing services. The research model was generated based on Technology Acceptance Model (TAM) with resistance variable, and verified statistically by undertaking a survey about the perception of personal users. As the results of this analysis, we could find the structural relationship among the factors affecting adoption of the cloud computing service. We found that the expectation of ubiquity as a representative function of the cloud computing service meaningfully affected the perceived ease of use and resistance, and that the relativeness with existing services also meaningfully affected the perceived ease of use, but not the resistance. In addition, the moderating effects of use experience in the path leading from the perceived ease of use and resistance to the intention of use were identified. This study will provide diverse implications for the companies providing personalized cloud computing services.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.379-394
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• 2013

Cloud computing is an evolving computing paradigm that has influenced every other entity in the globalized industry, whether it is in the public sector or the private sector. Considering the growing importance of cloud, finding new ways to improve cloud services is an area of concern and research focus. The limitation of the available Virtual Machine Load balancing policies for cloud is that they do not save the state of the previous allocation of a virtual machine to a request from a Userbase and the algorithm requires execution each time a new request for Virtual Machine allocation is received from the Userbase. This problem can be resolved by developing an efficient virtual machine load balancing algorithm for the cloud and by doing a comparative analysis of the proposed algorithm with the existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4280-4300
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• 2017

The fact that cloud computing services have been proposed in recent years, organizations and individuals face with various challenges and problems such as how to migrate applications and software platforms into cloud or how to ensure security of migrated applications. This study reviews the current challenges and open issues in cloud computing, with the focus on autonomic resource management especially in federated clouds. In addition, this study provides recommendations and research roadmaps for scientific activities, as well as potential improvements in federated cloud computing. This survey study covers results achieved through 190 literatures including books, journal and conference papers, industrial reports, forums, and project reports. A solution is proposed for autonomic resource management in the federated clouds, using machine learning and statistical analysis in order to provide better and efficient resource management.

• Convergence Security Journal
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• v.10 no.4
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• pp.77-82
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• 2010

Cloud computing is defined as numerous concepts by research institutions and scholars. However, due to the present business trend in the IT sector, emphasizing on cost and efficiency, cloud computing has been defined as a form of computing which can provide extendable mass storage components in the virtual environment. As a result, security issues have been arising due to the variety of cloud computing services provided by the industries. This paper aims to analyze the weaknesses such as security techniques and inquiries, and personal information protection required for various cloud computing services.

• Journal of information and communication convergence engineering
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• v.13 no.1
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• pp.15-20
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• 2015

The limited battery power in the mobile environment, lack of sufficient wireless bandwidth, limited resources of mobile terminals, and frequent breakdowns of the wireless network have become major hurdles in the development of mobile cloud computing (MCC). In order to solve the abovementioned problems, This paper propose a proxy-based MCC framework by adding a proxy server between mobile devices and cloud services to optimize the access to cloud services by mobile devices on the network transmission, application support, and service mode levels. Finally, we verify the effectiveness of the developed framework through an experimental analysis. This framework can ensure that mobile users have efficient access to cloud services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.263-279
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• 2024

With the increasing trend of Cloud migration, security threats in the Cloud computing environment have also experienced a significant increase. Consequently, the importance of efficient incident investigation through log data analysis is being emphasized. In Cloud environments, the diversity of services and ease of resource creation generate a large volume of log data. Difficulties remain in determining which events to investigate when an incident occurs, and examining all the extensive log data requires considerable time and effort. Therefore, a systematic approach for efficient data investigation is necessary. CloudTrail, the Amazon Web Services(AWS) logging service, collects logs of all API call events occurring in an account. However, CloudTrail lacks insights into which logs to analyze in the event of an incident. This paper proposes an automated analysis framework that integrates Cloud Matrix and event information for efficient incident investigation. The framework enables simultaneous examination of user behavior log events, event frequency, and attack information. We believe the proposed framework contributes to Cloud incident investigations by efficiently identifying critical events based on the ATT&CK Framework.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.71-87
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• 2007

In this study, 14 heavy snow events in Yeongdong area which are local phenomena are analyzed using MODIS cloud products provided from NASA/GSFC. The clouds of Yeongdong area at observed at specific time by MODIS are classified into A, B, C Types, based on the characteristic of cloud properties: cloud top temperature, cloud optical thickness, Effective Particle Radius, and Cloud Particle Phase. The analysis of relations between cloud properties and precipitation amount for each cloud type show that there are statistically significant correlations between Cloud Optical Thickness and precipitation amount for both A and B type and also significant correlation is found between Cloud Top Temperature and precipitation amount for A type. However, for C type there is not any significant correlations between cloud properties and precipitation amount. A-type clouds are mainly lower stratus clouds with small-size droplet, which may be formed under the low level cold advection derived synoptically in the East sea. B-type clouds are developed cumuliform clouds, which are closely related to the low pressure center developing over the East sea. On the other hand, C-type clouds are likely multi-layer clouds, which make satellite observation difficult due to covering of high clouds over low level clouds directly related with Yeongdong heavy snow. It is, therefore, concluded that MODIS cloud products may be useful except the multi-layer clouds for understanding the mechanism of heavy snow and estimating the precipitation amount from satellite data in the case of Yeongdong heavy snow.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.103-110
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• 2022

Since satellite images generally include clouds in the atmosphere, it is essential to detect or mask clouds before satellite image processing. Clouds were detected using physical characteristics of clouds in previous research. Cloud detection methods using deep learning techniques such as CNN or the modified U-Net in image segmentation field have been studied recently. Since image segmentation is the process of assigning a label to every pixel in an image, precise pixel-based dataset is required for cloud detection. Obtaining accurate training datasets is more important than a network configuration in image segmentation for cloud detection. Existing deep learning techniques used different training datasets. And test datasets were extracted from intra-dataset which were acquired by same sensor and procedure as training dataset. Different datasets make it difficult to determine which network shows a better overall performance. To verify the effectiveness of the cloud detection network such as Cloud-Net, two types of networks were trained using the cloud dataset from KOMPSAT-3 images provided by the AIHUB site and the L8-Cloud dataset from Landsat8 images which was publicly opened by a Cloud-Net author. Test data from intra-dataset of KOMPSAT-3 cloud dataset were used for validating the network. The simulation results show that the network trained with KOMPSAT-3 cloud dataset shows good performance on the network trained with L8-Cloud dataset. Because Landsat8 and KOMPSAT-3 satellite images have different GSDs, making it difficult to achieve good results from cross-sensor validation. The network could be superior for intra-dataset, but it could be inferior for cross-sensor data. It is necessary to study techniques that show good results in cross-senor validation dataset in the future.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.261-266
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• 2022

The full-fledged digital age derived from the fourth industrial revolution and the impact of COVID-19 lead to changes in various fields, including companies. In other words, the importance of cloud computing is being emphasized in the rapidly changing digital environment due to the rapid growth of the cloud market due to the rapid increase in digital services. The cloud may be one of the representative strategies for sustainable growth and survival in various fields as well as related industries. Although there have been a variety of studies on the cloud, the tendency of them has been not been adequately examined. This paper, therefore, analyzed the tendency of studies on the cloud computing. by using SCOPUS, the database of overseas academic journals using both R and LAD technique. The findings showed that many studies with high interest in the cloud computing have been conducted, the cloud computing were most often drawn from an analysis on key words. Moreover, various key words, including cloud, cloud and computing, data and computing were drawn, except for the theme of cloud computing. It is expected that could be used as a basic data, in that they provide the foundation for activating the related industries in terms of practice of the cloud computing.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.155-160
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• 2013

The global recession circumstances, cloud computing has emerged as a new paradigm in the business IT sector. This paper explores the analysis of cloud service adoption Intension of Korean firms. Especially, we focus on Infrastructure as a Service (IaaS) among cloud services and apply TAM (Technology Acceptance Model) and VAM (Value-based Adoption Model) to analyze cloud service adoption intension The proposed exploratory model tests a number of hypotheses to understand the importance factors of IaaS adoption intension with TAM and VAM included additional cloud service characteristics such as scalability, agility, security, efficiency and reliability. Eventually, the findings of this study can not only help company users gain insights into IaaS adoption, but also help cloud service providers to develop their service effectively and improve marketing strategy in B2B cloud service market.