• Journal of Digital Convergence
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• v.10 no.11
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• pp.59-65
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• 2012

The evolution of cloud computing service over the recent years is potentially one of the major advances in information and communication technology. However, if cloud computing service is to achieve its potential, there needs to be a clear understanding of the various issues such as service security, performance and availability and so on, both from the perspectives of the providers and the consumers of the cloud service. As more and more information on individuals, companies and public sectors are placed in the cloud service, concerns are beginning to grow about just how safe and reliable an environment it is. In order to overcome these situations, the Korea cloud service certification system and U.S. FedRAMP were performed in each country. This paper aims at comparing and analyzing between Korean cloud service certification systems and U.S. FedRAMP and describing the difference between them. Eventually, we propose the improvement strategy of Korea cloud service certification systems based on the comparison results between them.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Internet Computing and Services
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• v.15 no.1
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• pp.29-43
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• 2014

The development of NGS technologies, such as scientific workflows, has reduced the time required for decoding DNA sequences. Although the automated technologies change the genome sequence analysis environment, limited computing resources still pose problems for the analysis. Most scientific workflow systems are pre-built platforms and are highly complex because a lot of the functions are implemented into one system platform. It is also difficult to apply components of pre-built systems to a new system in the cloud environment. Cloud computing technologies can be applied to the systems to reduce analysis time and enable simultaneous analysis of massive DNA sequence data. Web service techniques are also introduced for improving the interoperability between DNA sequence analysis systems. The workflow-based middleware, which supports Web services, DBMS, and cloud computing, is proposed in this paper for expecting to reduceanalysis time and aiding lightweight virtual instances. It uses DBMS for managing the pipeline status and supporting the creation of lightweight virtual instances in the cloud environment. Also, the RESTful Web services with simple URI and XML contents are applied for improving the interoperability. The performance test of the system needs to be conducted by comparing results other developed DNA analysis services at the stabilization stage.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.721-723
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• 2015

Recently importance and scalability of cloud computing technology is rapidly emerging, it tends to have applied to various information systems. Many services have been made in mobile cloud environment, and medical information service quick application is required. When the medical information is important information of individuals to leakage, because you can cause serious problems, the problem of secure authentication procedures must be resolved. Therefore, in this paper, we propose an authentication scheme that can be more efficiently manage the medical information system in a mobile cloud environment.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.179-186
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• 2017

Managing students' data is a high prioritized duty of the university administration since most of the school affairs are proceed based on that database. Universities have invested in IT assets such as servers, storage, database, and networks. However, continuing investment in IT infrastructure is impossible due to limited budget and rapid changes in the educational environment. As cloud computing diffuse, universities are trying to reduce costs and improve efficiency by increasing server utilization, unlike when physically investing. We designed a hypothetical academic information management system based on cloud computing by utilizing the advanced server virtualization technology. This administrative cloud system allows universities to improve the availability of the system with low cost. The system demonstrates its flexibility of using data resources and immediacy of resumption.

• Journal of Information Processing Systems
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• v.17 no.4
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• pp.675-689
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• 2021

Nowadays, cloud computing is being adopted for more organizations. However, since cloud computing has a virtualized, volatile, scalable and multi-tenancy distributed nature, it is challenging task to perform attack detection in the cloud following conventional processes. This work proposes a solution which aims to collect web server logs by using Flume and filter them through Spark Streaming in order to only consider suspicious data or data related to denial-of-service attacks and reduce the data that will be stored in Hadoop Distributed File System for posterior analysis with the frequent pattern (FP)-Growth algorithm. With the proposed system, we can address some of the difficulties in security for cloud environment, facilitating the data collection, reducing detection time and consequently enabling an almost real-time attack detection.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.9
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• pp.189-196
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• 2020

Recently, GPU cloud computing technology applying GPU(Graphics Processing Unit) devices to virtual machines is widely used in the cloud environment. In a cloud environment, GPU devices assigned to virtual machines can perform operations faster than CPUs through massively parallel processing, which can provide many benefits when operating high-performance computing services in a variety of fields in a cloud environment. In a cloud environment, a GPU device can help improve the performance of a virtual machine, but the virtual machine scheduler, which is based on the CPU usage time of a virtual machine, does not take into account GPU device usage time, affecting the performance of other virtual machines. In this paper, we test and analyze the performance degradation of other virtual machines due to the virtual machine that performs GPGPU(General-Purpose computing on Graphics Processing Units) task in the direct path based GPU virtualization environment, which is often used when assigning GPUs to virtual machines in cloud environments. Then to solve this problem, we propose a GPGPU task management method for a virtual machine.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.179-187
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• 2011

Multiple roadmap DB providers are already available in these days, and try to reduce unknown roads in their own roadmaps. However, cooperation models or Win-Win approaches between roadmap providers are not considered yet. Thus, In this paper, We proposed a cloud-oriented real-time roadmap generation and update method between heterogeneous navigation systems for unknown roads. With the proposed method, the roadmap DB providers update the own roadmap DB for navigation systems in real time. Also, they can provide the complete roadmap without unknown roads to users instantly. Therefore, the proposed method can reduce the costs of an actual traveling test and the maintenance for the roadmap DB provides. Thus, the cloud-oriented roadmap generation method can more efficiently update the unknown road information.

• Annual Conference of KIPS
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• 2015.04a
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• pp.121-124
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• 2015

Recent global and cloud computing render the Internet and Web application to be a paramount field since it is uncomplicated to access and less time and space limitation. On the other hand, a growing number of computations using grid computing techniques indicates the requirements and quantities of large-scale computations are becoming foremost progressively. Therefore, that will be much practicable if there is a Web-based service that could provide Grid computing functions. In this paper, Several similar Web-based cloud and parallel computing systems will be discussed and a model of Web application termed GCaaS which supports grid computing services will be introduced.

• ETRI Journal
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• v.37 no.2
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• pp.348-358
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• 2015

Mobile devices have been widespread and become very popular with connectivity to the Internet, and a lot of desktop PC applications are now aggressively ported to them. Unfortunately, mobile devices are often vulnerable to malicious attacks due to their common usage and connectivity to the Internet. Therefore, the demands on the development of mobile security systems increase in accordance with advances in mobile computing. However, it is very hard to run a security program on a mobile device all of the time due the device's limited computational power and battery life. To overcome these problems, we propose a novel mobile security scheme that migrates heavy computations on mobile devices to cloud servers. An efficient data transmission scheme for reducing data traffic between devices and servers over networks is introduced. We have evaluated the proposed scheme with a mobile device in a cloud environment, whereby it achieved a maximum speedup of 13.4 compared to a traditional algorithm.