• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.61-70
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• 2024

Cloud Computing is internet-based computing, where the users are provided with whatever service they need from the resources, software, and information. Recently, the security of cloud computing is considered as one of the major issues for both cloud service providers CSP and end-users. Privacy and highly confidential data make many users refuse to store their data within cloud computing, since data on cloud computing is not dully secured. The cryptographic algorithm is a technique which is used to maintain the security and privacy of the data on the cloud. In this research, we applied eight different cryptographic algorithms on Xen and KVM as hypervisors on cloud computing, to be able to measure and compare the performance of the two hypervisors. Response time and CPU utilization while encryption and decryption have been our aspects to measure the performance. In terms of response time and CPU utilization, results show that KVM is more efficient than Xen on average at 11.5% and 11% respectively. While TripleDES cryptographic algorithm shows a more efficient time response at Xen hypervisor than KVM.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.512-520
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• 2013

In this paper, the control algorithm which can resolve the unfairness in network performance of the virtual machine arised from the CPU scheduling in cloud datacenter has been proposed. We first describe the evaluation and analysis results of the network unfairness phenomenon of virtual machine through the heterogeneous cloud datacenter testbed and we propose the control algorithm which can guarantee the fairness of the network performance based on the PID control scheme. Through the implementation and evaluation results, we verify the performance of the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.823-826
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• 2012

In this paper, we propose an efficient network virtualization model in cloud computing environments. Virtualization is a key technology for the implementation of service-oriented architecture. It is a standardized framework that can be reused or integrated with changing business priorities through a IT infrastructure. Network virtualization has emerged as an important technical issues of the future virtualization technology in Internet. The concept of network virtualization and related technologies stay in ambiguous status since network virtualization is in its early stage. Thus, we propose a network virtualization model for cloud environment by analyzing the existing network virtualization technologies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.603-606
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• 2013

Mobile and wireless communication technologies to the development of a variety of next-generation mobile networks such as smart phones, tablet PC and mobile terminals will coexist various access networks. In a variety of network service continuity and quality of service degradation due to network conditions, and the EPC network traffic overload phenomenon still remains a problem. In this paper, the EPC network traffic overload in distributed cloud servers is proposed to select. Our proposed scheme using an efficient network handover can provide optimal service according to the network condition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.501-511
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• 2013

Recently, the visualization method of social network service have been only focusing on presentation of visualizing network data, which the methods do not consider an efficient processing speed and computational complexity for increasing at the ratio of arithmetical of a big data regarding social networks. This paper proposes a cloud based on visualization method to visualize a user focused hierarchy relationship between user's nodes on social network. The proposed method can intuitionally understand the user's social relationship since the method uses fuzzy to represent a hierarchical relationship of user nodes of social network. It also can easily identify a key role relationship of users on social network. In addition, the method uses hadoop and hive based on cloud for distributed parallel processing of visualization algorithm, which it can expedite the big data of social network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4682-4705
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• 2020

The Unmanned Aerial Vehicles (UAV) networks consisting of low-cost UAVs are very vulnerable to smart jammers that can choose their jamming policies based on the ongoing communication policies accordingly. In this article, we propose a novel cloud and edge-aided mobile communication scheme for low-cost UAV network against smart jamming. The challenge of this problem is to design a communication scheme that not only meets the requirements of defending against smart jamming attack, but also can be deployed on low-cost UAV platforms. In addition, related studies neglect the problem of decision-making algorithm failure caused by intermittent ground-to-air communication. In this scheme, we use the policy network deployed on the cloud and edge servers to generate an emergency policy tables, and regularly update the generated policy table to the UAVs to solve the decision-making problem when communications are interrupted. In the operation of this communication scheme, UAVs need to offload massive computing tasks to the cloud or the edge servers. In order to prevent these computing tasks from being offloaded to a single computing resource, we deployed a lightweight game algorithm to ensure that the three types of computing resources, namely local, edge and cloud, can maximize their effectiveness. The simulation results show that our communication scheme has only a small decrease in the SINR of UAVs network in the case of momentary communication interruption, and the SINR performance of our algorithm is higher than that of the original Q-learning algorithm.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.55-61
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• 2017

Cloud computing is cost-effective in terms of system configuration and maintenance and does not require special IT skills for management. Also, cloud computing provides an access control setting where SSO is adopted to secure user convenience and availability. As the SSO user authentication structure of cloud computing is exposed to quite a few external security threats in wire/wireless network integrated service environment, researchers explore technologies drawing on distributed SSO agents. Yet, although the cloud computing access control using the distributed SSO agents enhances security, it impacts on the availability of services. That is, if any single agent responsible for providing the authentication information fails to offer normal services, the cloud computing services become unavailable. To rectify the environment compromising the availability of cloud computing services, and to protect resources, the current paper proposes a security policy that controls the authority to access the resources for cloud computing services by applying the authentication policy of user authentication agents. The proposed system with its policy of the authority to access the resources ensures seamless and secure cloud computing services for users.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1041-1051
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• 2012

Attack on Cloud Computing, an intensive service solution using network infrastructure recently released, generates service breakdown or intrusive incidents incapacitating developmental platforms, web-based software, or resource services. Therefore, it is needed to conduct research on security for the operational information of three kinds of services (3S': laaS, PaaS, SaaS) supported by the Cloud Computing system and also generated data from the illegal attack on service blocking. This paper aims to build a system providing optimal services as a 4-stage defensive method through the test on the attack and defense of Cloud Computing services. It is a defense policy that conducts 4-stage, orderly and phased access control as follows: controlling the initial access to the network, controlling virtualization services, classifying services for support, and selecting multiple routes. By dispersing the attacks and also monitoring and analyzing to control the access by stage, this study performs defense policy realization and analysis and tests defenses by the types of attack. The research findings will be provided as practical foundational data to realize Cloud Computing service-based defense policy.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.374-388
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• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.