• Proceedings of the Korean Society of Computer Information Conference
• /
• 2011.06a
• /
• pp.367-368
• /
• 2011

본 논문에서는 PON(Passive Optical Network)망의 장애진단을 위한 임베디드 네트워크 제어기를 설계하고 구현하였다. 본 연구에서 구현한 임베디드 네트워크 제어기는 PON망의 가장 말단인 수용가의 인터넷 공유기에 부착되어 수용가의 인터넷 선로장애를 진단할 수 있도록 구현함으로서 인터넷 서비스 제공자(ISP)의 NMS가 점검할 수 없는 영역까지 장애를 진단할 수 있다. 또한, 임베디드 네트워크 제어기는 PON망의 장애진단 뿐만 아니라 수용가의 가전제품 전원제어나 다양한 센서를 부착하여 제어할 수 있도록 제작하여 간단한 홈오토메이션 제어기로 활용할 수 있도록 설계하였다.

• Journal of information and communication convergence engineering
• /
• v.19 no.4
• /
• pp.205-213
• /
• 2021

The properties of a scale-free network are little known; its node degree following a power-law distribution is among its few known properties. By selecting real-field scale-free networks from a network dataset and comparing them to other networks, such as random and non-scale-free networks, the topology characteristics of scale-free networks are identified. The assortative coefficient is identified as a key metric of a scale-free network. It is also identified that most scale-free networks have negative assortative coefficients. Traditional generation models of scale-free networks are evaluated based on the identified topology characteristics. Most representative models, such as BA and Holme&Kim, are not effective in generating real-field scale-free networks. A link-rewiring method is suggested that can control the assortative coefficient while preserving the node degree sequence. Our analysis reveals that it is possible to effectively reproduce the assortative coefficients of real-field scale-free networks through link-rewiring.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.9
• /
• pp.35-42
• /
• 2019

In this paper, we propose an approach to apply network-based moving target defense into Internet of Things (IoT) networks. The IoT is a technology that provides the high interconnectivity of things like electronic devices. However, cyber security risks are expected to increase as the interconnectivity of such devices increases. One recent study demonstrated a man-in-the-middle attack in the statically configured IoT network. In recent years, a new approach to cyber security, called the moving target defense, has emerged as a potential solution to the challenge of static systems. The approach continuously changes system's attack surface to prevent attacks. After analyzing IPv4 / IPv6-based moving target defense schemes and IoT network-related technologies, we present our approach in terms of addressing systems, address mutation techniques, communication models, network configuration, and node mobility. In addition, we summarize the direction of future research in relation to the proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.1
• /
• pp.366-381
• /
• 2020

Steganography has been successfully employed in various applications, e.g., copyright control of materials, smart identity cards, video error correction during transmission, etc. Deep learning-based steganography models can hide information adaptively through network learning, and they draw much more attention. However, the capacity, security, and robustness of the existing deep learning-based steganography models are still not fully satisfactory. In this paper, three models for different cases, i.e., a basic model, a secure model, a secure and robust model, have been proposed for different cases. In the basic model, the functions of high-capacity secret information hiding and extraction have been realized through an encoding network and a decoding network respectively. The high-capacity steganography is implemented by hiding a secret image into a carrier image having the same resolution with the help of concat operations, InceptionBlock and convolutional layers. Moreover, the secret image is hidden into the channel B of carrier image only to resolve the problem of color distortion. In the secure model, to enhance the security of the basic model, a steganalysis network has been added into the basic model to form an adversarial network. In the secure and robust model, an attack network has been inserted into the secure model to improve its robustness further. The experimental results have demonstrated that the proposed secure model and the secure and robust model have an overall better performance than some existing high-capacity deep learning-based steganography models. The secure model performs best in invisibility and security. The secure and robust model is the most robust against some attacks.

• Journal of Korean Library and Information Science Society
• /
• v.44 no.4
• /
• pp.443-463
• /
• 2013

Korea Institute of Science and Technology Information(KISTI) provides domestic researchers with science and technology information through NDSL Information Document Service(NIDS) network to improve research productivity in Korea. University libraries and information centers of research institutes are playing a major role in the NIDS collaboration network. In this study, we examined the relationship among the participating organizations for document delivery service using the social network analysis(SNA) method. Centrality of each organization in the NIDS network was analyzed with the indexes such as degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The research results show that KISTI, KAIST, POSTECH, and FRIC are located at the center of the NIDS network. Based on the research results, this paper suggests several directions for improvement of document delivery service.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.1
• /
• pp.144-149
• /
• 2017

This study empirically analyzes the effect of IT SME ventures' external information network diversity on their production period reduction and productivity improvement generated from technology development. This research constructs a mediating model based on the open innovation perspective and tests it with the 138 samples of South Korean IT SME ventures based on the ordinary least squares regression. This research is expected to make a good contribution by shedding a new light on the following three points about the critical role of IT SME ventures' external information network diversity in increasing their production period reduction and productivity improvement generated from technology development which has scarcely been illuminated in the extant studies in the field of the management of technology for SMEs. First, IT SME ventures' external information network diversity positively influences their production period reduction. Second, the external information network diversity positively influences IT SMEs' ventures' productivity improvement. Third, IT SME ventures' production period reduction partially mediates the influence of IT SME ventures' external network diversity on their productivity improvement. These three fresh points are expected to provide useful theoretical and practical implications. Related to the theoretical implication, this research provides a fresh implication that IT SME ventures' external information network diversity positively influences not only their production period reduction but also productivity improvement generated from technology development. Concerning the practical implication, this study suggests that the CEOs in IT SME ventures make strategic efforts to use more diverse external information sources in order to increase their production period reduction and productivity improvement generated from technology development.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.10C
• /
• pp.1011-1022
• /
• 2006

WSN(Wireless Sensor Network) performs to detect and collect environmental information for one purpose. The WSN is composed of a sink node and several sensor nodes and has a constraint in an aspect of energy consumption caused by limited battery resource. So many required mechanisms in WSN should consider the remaining energy condition. To deploy WSN, tile collected information is required to protect from an adversary over the network in many cases. The security mechanism should be provided for collecting the information over the network. we propose asynchronized key management considering energy efficiency over WSN. The proposed key management is focused on independence and difference of the keys used to deliver the information over several routes over the network, so disclosure of any key does not results in exposure of total key information over the overall WSN. Also, we use hash function to update key information for energy efficiency Periodically. We define the insecurity for requested security Properties and Proof that the security properties are guaranteed. Also, we evaluate and analyze the energy efficiency for the proposed mechanism.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1706-1725
• /
• 2024

The unique U-shaped structure of U-Net network makes it achieve good performance in image segmentation. This network is a lightweight network with a small number of parameters for small image segmentation datasets. However, when the medical image to be segmented contains a lot of detailed information, the segmentation results cannot fully meet the actual requirements. In order to achieve higher accuracy of medical image segmentation, a novel improved U-Net network architecture called multi-scale encoder-decoder U-Net+ (MEDU-Net+) is proposed in this paper. We design the GoogLeNet for achieving more information at the encoder of the proposed MEDU-Net+, and present the multi-scale feature extraction for fusing semantic information of different scales in the encoder and decoder. Meanwhile, we also introduce the layer-by-layer skip connection to connect the information of each layer, so that there is no need to encode the last layer and return the information. The proposed MEDU-Net+ divides the unknown depth network into each part of deconvolution layer to replace the direct connection of the encoder and decoder in U-Net. In addition, a new combined loss function is proposed to extract more edge information by combining the advantages of the generalized dice and the focal loss functions. Finally, we validate our proposed MEDU-Net+ MEDU-Net+ and other classic medical image segmentation networks on three medical image datasets. The experimental results show that our proposed MEDU-Net+ has prominent superior performance compared with other medical image segmentation networks.

• The KIPS Transactions:PartC
• /
• v.13C no.6 s.109
• /
• pp.775-784
• /
• 2006

As the scale of network expands largely, most of users consume much cost for complex network that composes various network environments. In this paper, we propose a network management framework employing active management program generation facilities in order to reduce the cost and time for developing a network management program. The proposed framework consists of several basic components including configuration management, performance management, and fault management for real time network management. Besides those components, it has an added active program generator in order to actively create information of network objects that are managed through the basic components. Our framework manages the network with SNMP manager using the information of network objects. This information is generated automatically instead of the existing manual manner. We shows that the cost and time for developing a network management program is reduced through the construction and operations on the real network.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.13 no.4
• /
• pp.125-139
• /
• 2017

In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.