• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1027-1036
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• 2020

In this study, a cloud-based processing method using Agisoft Metashape, a commercial software, and Amazon web service, a cloud computing service, is introduced and evaluated to quickly generate high-precision 3D realistic data from large volume UAV images acquired in disaster sites. Compared with on-premises method using a local computer and cloud services provided by Agisoft and Pix4D, the processes of aerial triangulation, 3D point cloud and DSM generation, mesh and texture generation, ortho-mosaic image production recorded similar time duration. The cloud method required uploading and downloading time for large volume data, but it showed a clear advantage that in situ processing was practically possible. In both the on-premises and cloud methods, there is a difference in processing time depending on the performance of the CPU and GPU, but notso much asin a performance benchmark. However, it wasfound that a laptop computer equipped with a low-performance GPU takes too much time to apply to in situ processing.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.608-611
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• 2012

In this paper, a mobile cloud-based agent model is proposed in order to overcome the resource constraints and the difficulties of installing a variety of complex applications in mobile devices. The proposed software architecture overcomes the disadvantages of mobile devices and maximizes usage of cloud services by implementing the function of mobile agent concept. The significant portion of the cloud services is run in the cloud server and the client application implemented in the mobile device can call and control appropriate service from the cloud server. Thus, the inherent weakness of the mobile can be overcome by executing the target applications on cloud nodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3293-3311
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• 2015

Cloud services are required to be composed as a single service to fulfill the workflow applications. Service composition in Cloud raises new challenges caused by the diversity of users with different QoS requirements and vague preferences, as well as the development of cloud computing having geographically distributed characteristics. So the selection of the best service composition is a complex problem and it faces trade-off among various QoS criteria. In this paper, we propose a Cloud service composition approach based on evolutionary algorithms, i.e., NSGA-II and MOPSO. We utilize the combination of multi-objective evolutionary approaches and Decision-Making method (AHP) to solve Cloud service composition optimization problem. The weights generated from AHP are applied to the Crowding Distance calculations of the above two evolutionary algorithms. Our algorithm beats single-objective algorithms on the optimization ability. And compared with general multi-objective algorithms, it is able to precisely capture the users' preferences. The results of the simulation also show that our approach can achieve a better scalability.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.117-128
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• 2014

In this paper, we proposed the necessary government policies for the SME's informatization as the computing environment evolves towards cloud computing. We started with the review of the current SME's computing environment and limitations of current policies, and then addressed the new roles, visions, and missions of the government for the SME informatization. Based upon these arguments, we proposed the mid-term vision and designed the architecture for the cloud computing-based services for SMEs. The major contribution of this paper is to extend the enterprise-based informatization strategies to the context of the government policies so that the government can adopt our arguments as guidelines for the future policies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.295-311
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• 2023

Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.1
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• pp.9-22
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• 2022

Virtual Reality(VR) technology is an interface technology that is actively used in various audio-visual-based applications by showing users a virtual world composed of computer graphics. Since VR-based applications are graphic processing-based applications, expensive computing devices equipped with Graphics Processing Unit(GPU) are essential for graphic processing. This incurs a cost burden on VR application users for maintaining and managing computing devices, and as one of the solutions to this, a method of operating services in cloud environments is being used. This paper proposes a performance management technique to address the problem of performance interference between containers owing to GPU resource competition in container-based high-performance cloud environments in which multiple containers share a single GPU. The proposed technique reduces performance deviation due to performance interference, helping provide uniform performance-based remote VR services for users. In addition, this paper verifies the efficiency of the proposed technique through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5174-5190
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• 2016

Recent years have witnessed an explosive growth of mobile devices, mobile cloud computing services offered by these devices and the remote clouds behind them. In this paper, we noticed ultra-low latency service, as a type of mobile cloud computing service, requires extremely short delay constraints. Hence, such delay-sensitive applications should be satisfied with strong QoS guarantee. Existing solutions regarding this problem have poor performance in terms of throughput. In this paper, we propose an end-to-end bandwidth resource reservation via software defined scheduling inspired by the famous SDN framework. The main contribution of this paper is the end-to-end resource reservation and flow scheduling algorithm, which always gives priority to delay sensitive flows. Simulation results confirm the advantage of the proposed solution, which improves the average throughput of ultra-low latency flows.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.11-14
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• 2018

본 논문에서는 다양한 과학기술 분야의 연구자들이 R&D 수행에 있어서 필요한 인프라로서 클라우드 서비스를 활발히 이용하도록 하기 위해서 요구되는 정책적 기술적 개선 방안을 도출하였다. 이를 위해서 R&D를 위한 클라우드 서비스의 해외 사례를 조사하고, 국내 출연(연) 대학 등 연구현장의 다양한 분야의 연구자들로부터 클라우드 서비스에 대한 인식 조사 및 수요 분석을 실시하였다. 이러한 조사 분석 결과 과학기술 및 공학 ICT 등 다양한 분야의 연구에서 요구되는 수요를 충족할 수 있는 클라우드 서비스의 필요성을 도출하였으며, 보안 및 행정제도 등에 있어서도 개선점을 도출할 수 있었다. 연구를 통하여 우리는 국가 R&D를 수행하는 연구자들이 기존 클라우드 서비스에 대한 제약을 해소하고 더 나은 연구환경을 지원받도록 하기 위한 R&D 사이버인프라스트럭쳐로서의 클라우드 서비스를 제안하였다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.128-130
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• 2021

Smart cities collect data from thousands of IoT-based sensor devices for intelligent application-based services. Centralized cloud servers support application tasks with higher computation resources but introduce network latency. Fog layer-based data centers bring data processing at the edge, but fewer available computation resources and poor task allocation strategy prevent real-time data analysis. In this paper, tasks generated from devices are distributed as high resource and low resource intensity tasks. The novelty of this research lies in deploying a virtual node assigned to each cluster of IoT sensor machines serving a joint application. The node allocates tasks based on the task intensity to either cloud-computing or fog computing resources. The proposed Task Allocation Strategy provides seamless allocation of jobs based on process requirements.