• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.232-235
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• 2015

In recent years, mobile devices are equipped with functionalities comparable to those computers. However, mobile devices have limited resources due to constraints, such as low processing power, limited memory, unpredictable connectivity, and limited battery life. To enhance the capacity of mobile devices, an interesting idea is to use cloud computing and virtualization techniques to shift the workload from mobile devices to a computational infrastructure. Those techniques consist of migrating resource-intensive computations from a mobile device to the resource-rich cloud, or server (called nearby infrastructure). In order to achieve their goals, researchers designed mobile cloud applications models (examples: CloneCloud, Cloudlet, and Weblet). In this paper, we want to highlight on cloudlet architecture (nearby infrastructure with mobile device), its methodology and discuss about the impact of distance between cloudlet and mobile device in our work design.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.9
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• pp.590-597
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• 2014

This paper proposes multi-camera system that relies on 3D views for panoramic video and distribution method about panoramic video generation algorithm by using cloud computing. The proposed monitoring system monitors the projected 3D model view, instead of individual 2D views, to detect image distortions. This can minimize compensation errors caused by parallax, thereby improving the quality of the resulting panoramic video. Panoramic video generation algorithm can be divided into registration part and compositing part. Therefore we propose off-loading method of these parts with cloud computing for panoramic video service.

• Journal of Korea Technology Innovation Society
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• v.22 no.2
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• pp.265-281
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• 2019

With the advent of the Internet-based economy, the dramatic changes in consumption patterns have been witnessed during the last decades. The seminal change has led by Data Technology, the integrated platform of mobile, online, offline and artificial intelligence, which remained unchallenged. In this paper, I use data analysis tool (TexTom) in order to articulate the definitfite notion of data technology from Internet sources. The data source is collected for last three years (November 2015 ~ November 2018) from Google and Naver. And I have derived several key keywords related to 'Data Technology'. As a result, it was found that the key keyword technologies of Big Data, O2O (Offline-to-Online), AI, IoT (Internet of things), and cloud computing are related to Data Technology. The results of this study can be used as useful information that can be referred to when the Data Technology age comes.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.221-228
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• 2019

The importance of security for virtualization products has been increased with the activation policy of cloud computing and it is necessary to analyze cyber security threats and develop security requirements for virtualization products to provide with more secure cloud environments. This paper is a preliminary study with the purpose of developing security functional requirements through analyzing security features and cyber security threats as well as comparison of foreign countries' cases for virtualization products. To do this, the paper compares evaluation schemes for virtualization products in US and UK foreign countries, and analyzes the cyber security threats, security objectives and security requirements in both countries. Furthermore, it proposes the essential checking items and processes for developing security functional requirements about security features of virtualization products to contribute to its more secure development and the establishment of related security evaluation standards.

• Convergence Security Journal
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• v.21 no.3
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• pp.25-30
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• 2021

Due to the current COVID-19 pandemic, companies and institutions are introducing virtual desktop technology, one of the logical network separation technologies, to establish a safe working environment in a situation where remote work is provided. With the introduction of virtual desktop technology, companies and institutions can operate the network separation environment more safely and effectively, and can access the business network quickly and safely to increase work efficiency and productivity. However, when introducing virtual desktop technology, there is a cost problem of high-spec server, storage, and license, and it is necessary to supplement in terms of operation and management. As a countermeasure to this, companies and institutions are shifting to cloud computing-based technology, virtual desktop service (DaaS, Desktop as a Service). However, in the virtual desktop service, which is a cloud computing-based technology, the shared responsibility model is responsible for user access control and data security. In this paper, based on the shared responsibility model in the virtual desktop service environment, we propose a cloud-based hardware security module (Cloud HSM) and edge-DRM proxy as an improvement method for user access control and data security.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.659-666
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• 2012

Recently, cloud computing service has become a rising issue in terms of utilizing sources more efficiently and saving costs. However, the service still has some limitations to be popularized because it lacks the verification towards security safety. In particular, the possibility to induce Denial of service is increasing as it is used as Zombie PC with exposure to security weakness of Guest OS's. This paper suggests how cloud system, which is implemented by Xen, detects intrusion caused by Denial of service using hypercall. Through the experiment, the method suggested by K-means and EM shows that two data, collected for 2 mins, 5 mins, 10mins and 20mins each, are distinguished 90% when collected for 2mins and 5mins while collected over 10mins are distinguished 100% successfully.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.333-344
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• 2022

In a container-based cloud environment, multiple containers can share a graphical processing unit (GPU), and GPU sharing can minimize idle time of GPU resources and improve resource utilization. However, in a cloud environment, GPUs, unlike CPU or memory, cannot logically multiplex computing resources to provide users with some of the resources in an isolated form. In addition, containers occupy GPU resources only when performing GPU operations, and resource usage is also unknown because the timing or size of each container's GPU operations is not known in advance. Containers unrestricted use of GPU resources at any given point in time makes managing resource contention very difficult owing to where multiple containers run GPU tasks simultaneously, and GPU tasks are handled in black box form inside the GPU. In this paper, we propose a container management technique to prevent performance degradation caused by resource competition when multiple containers execute GPU tasks simultaneously. Also, this paper demonstrates the efficiency of container management techniques that analyze and propose the problem of degradation due to resource competition when multiple containers execute GPU tasks simultaneously through experiments.

• KNOM Review
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• v.23 no.1
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• pp.51-62
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• 2020

Open IoT platform that shares communication infrastructure and provides cloud resources can flexibly reduce development period and cost of smart service. In this paper, as an open IoT platform, we propose a virtual IoT system based on edge computing that implements a virtual IoT device for a physical IoT device and allows service developers to interact with the virtual device. A management server in the edge cloud, near the IoT physical device, manages the creation, movement, and removal of virtual IoT devices corresponding to the physical IoT devices. This paper define the operations of the management server, the physical IoT device, and the virtual IoT device, which are major components of the virtual IoT system, and design the communication protocol required to perform the operations. Finally, through simulations, this paper evaluate the performance of the edge computing based virtual IoT system by confirming that each component performs the defined states and operations as designed.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.25-31
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• 2023

The brain-machine interface(BMI) is a next-generation interface that controls the device by decoding brain waves(also called Electroencephalogram, EEG), EEG is a electrical signal of nerve cell generated when the BMI user thinks of a command. The brain-machine interface can be applied to various smart devices, but complex computational process is required to decode the brain wave signal. Therefore, it is difficult to implement a brain-machine interface in an embedded system implemented in the form of an edge device. In this study, we proposed a new type of brain-machine interface system using IoT technology that only measures EEG at the edge device and stores and analyzes EEG data in the cloud computing. This system successfully performed quantitative EEG analysis for the brain-machine interface, and the whole data transmission time also showed a capable level of real-time processing.