• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.99-106
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• 2022

In this paper, we proposes digital content management technology in a cloud computing environment. proposes digital content management technology in a cloud computing environment. Computing services using networks are basic infrastructure services that cannot be missed in the era of the 4th Industrial Revolution. Financial services, digital content services, and industrial and home network services using smartphones are changing from services in the local area to a cloud service environment where the entire service is possible. Therefore, this study proposed a system to safely support digital content services suitable for cloud computing environments. The proposed system provides convenience and safety for users to access the system, protects the copyright of digital content authors, and provides a secure digital content distribution and management system. The purpose of this study is to stabilize and revitalize the digital content market by providing a digital content distribution structure suitable for the cloud computing environment.

• Convergence Security Journal
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• v.15 no.2
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• pp.63-68
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• 2015

Current ICT(Information & Communication Technology) infrastructures (Internet and server/client communication) are struggling for a wide variety of devices, services, and business and technology evolution. Cloud computing originated simply to request and execute the desired operation from the network of clouds. It means that an IT resource that provides a service using the Internet technology. It is getting the most attention in today's IT trends. In the distributed cloud environments, management costs for the network and computing resources are solved fundamentally through the integrated management system. It can increase the cost savings to solve the traffic explosion problem of core network via a distributed Micro DC. However, traditional flooding methods may cause a lot of traffic due to transfer to all the neighbor DCs. Restricted Path Flooding algorithms have been proposed for this purpose. In large networks, there is still the disadvantage that may occur traffic. In this paper, we developed Lightweight Path Flooding algorithm to improve existing flooding algorithm using hop count restriction.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.308-315
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• 2016

With the advancement in the area of cloud storage services as well as a tremendous growth of social networking sites, permission for one web service to act on the behalf of another has become increasingly vital as social Internet services such as blogs, photo sharing, and social networks. With this increased cross-site media sharing, there is a upscale of security implications and hence the need to formulate security protocols and considerations. Recently, OAuth, a new protocol for establishing identity management standards across services, is provided as an alternative way to share the user names and passwords, and expose personal information to attacks against on-line data and identities. Moreover, OwnCloud provides an enterprise file synchronizing and sharing that is hosted on user's data center, on user's servers, using user's storage. We propose a secure Social Networking Site (SSN) access based on OAuth implementation by combining two novel concepts of OAuth and OwnCloud. Security analysis and performance evaluation are given to validate the proposed scheme.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.35-43
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• 2020

Cloud computing integrates computing resources such as servers, storage, and networks with virtualization technology to provide suitable services according to user needs. Due to the structural characteristics of sharing physical resources based on virtualization technology, threats to availability can occur, so it is essential to respond to availability threats in cloud computing. Existing over-provisioning method is not suitable because it can generate idle resources and cause under-provisioning to degrade or disconnect service. System resources must be allocated in real-time according to the system load to guarantee the cloud system's availability. Through appropriate management measures, it is necessary to reduce the system load and increase the performance of the system. This paper analyzes the work response time according to the allocation or migration of virtual machines and discusses an efficient resource management method considering the system load.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.173-178
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• 2018

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

• Electronics and Telecommunications Trends
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• v.34 no.3
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• pp.23-33
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• 2019

Broadcast is evolving into media service aimed at user customization, personalization, and participation with high-quality broadcasting contents (4K/8K/AR/VR). A broadcast infrastructure is needed to engage with the competition for providing large-scaled media traffic process, platform performance for adaptive transcoding to diverse receivers, and intelligent service. Cloud service and virtualization in broadcast are becoming more valuable as the broadcasting environment changes and new high-level broadcasting services emerge. This document describes the examples of cloud and virtualization in the broadcast industry, and prospects the network virtualization of broadcast transmission infrastructure, especially terrestrial and cable networks.

• ETRI Journal
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• v.43 no.1
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• pp.53-63
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• 2021

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.138-140
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• 2020

As the IoT is applied to home and industrial networks, data generated by the IoT is being processed at the cloud edge. Intrusion detection function is very important because it can be operated by invading IoT devices through the cloud edge. Data delivered to the edge network in the cloud environment is traffic at the application layer. In order to determine the intrusion of the packet transmitted to the IoT, the intrusion should be detected at the application layer. This paper proposes the intrusion detection function at the application layer excluding normal traffic from IoT intrusion detection function. As the proposed method, we obtained the intrusion detection result by decision tree method and explained the detection result for each feature.