• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.267-271
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• 2010

Zigbee는 단말에 대한 경제성이 뛰어나고 저 전력통신을 이용하기 때문에 수명이 길다. Mesh, Tree, Star 등 다양한 방식의 토플리지 구조를 지원 하며 확장성이 뛰어나 군사적인 용도, 환경 모니터링 시스템 등 많은 분야에 사용되고 있다. 최근 스마트그리드환경을 구축함에 있어 Zigbee는 HAN(Home Area Network)에 표준으로 사용될 예정이며 현재는 Zigbee를 이용한 AMR(Automatic Meter Reading)을 시범 중에 있다. 일반적으로 ZIgbee Network은 ZC(Zigbee Coordinator), ZCH(Zigbee Cluster Head), ZE(Zigbee End Device) 3가지로 구성되며, Zigbee Network에서 발생할 수 있는 취약점은 허가되지 않은 디바이스의 접근, 라우터의 흐름을 조작하는 방법, ZC(Zigbee Coordinator)와 ZE(Zigbee End Device)사이의 키 전송 시 안전하지 않은 채널을 이용하여 전송되는 문제가 발생된다. 본 논문에서는, TCP(Third Party Center)를 이용함으로써, ZE와 ZC간의 키 생성 시 발생하는 취약점을 보완하였다. 또한 인증절차를 강화함으로써 ZE(Zigbee End Device)에서 발생 할 수 있는 취약점을 보완하고자 하였으며 RS(Register Server)를 이용하여 HAN에 존재하는 디바이스에 대하여 실시간 모니터링이 가능하게 하였다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.461-466
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• 2024

Korean local governments operates the participatory budgeting system autonomously. This study is to classify these entities into clusters. Among the diverse machine learning methodologies(Neural Network, Rule Induction(CN2), KNN, Decision Tree, Random Forest, Gradient Boosting, SVM, Naïve Bayes), the Support Vector Machine technique emerged as the most efficacious in the analysis of 2022 Korean municipalities data. The first cluster C1 is characterized by minimal committee activity but a substantial allocation of participatory budgeting; another cluster C3 comprises cities that exhibit a passive stance. The majority of cities falls into the final cluster C2 which is noted for its proactive engagement in. Overall, most Korean local government operates the participatory busgeting system in good shape. Only a small number of cities is less active in this system. We anticipate that analyzing time-series data from the past decade in follow-up studies will further enhance the reliability of classifying local government types regarding participatory budgeting.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.65-73
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• 2016

Peer-to-Peer (P2P) systems with super-peer overlay networks show combined advantages of both hybrid and pure P2P systems. Super-peer is a special peer acting as a server to a cluster of generic peers. Organizing a super-peer network is one of important issues for P2P systems with super-peer networks. Conventional P2P systems are based on two-level hierarchies of peers. One is a layer for generic peers and the other is for super-peers. And it is usual that super-peer networks have forms of random graphs. However, for accommodating a large-scale collection of generic peers, the super-peer network has also to be extended. In this paper, we propose a scheme of hierarchically constructing super-peer networks for large-scale P2P systems. At first, a two-level tree, called a simple super-peer network, is proposed, and then a scheme of generalizing and then extending the simple super-peer network to multi-level super-peer network is presented to construct a large-scale super-peer network. We call it an extended super-peer network. The simple super-peer network has several good features, but due to the fixed number of levels, it may have a scalability problem. Thus, it is extended to k-level tree of a super-peer network, called extended super-peer network. It shows good scalability and easy management of generic peers for large scale P2P system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5819-5840
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• 2018

Considering the topology of hierarchical tree structure, each cluster in WSNs is faced with various attacks launched by malicious nodes, which include network eavesdropping, channel interference and data tampering. The existing intrusion detection algorithm does not take into consideration the resource constraints of cluster heads and sensor nodes. Due to application requirements, sensor nodes in WSNs are deployed with approximately uncorrelated security weights. In our study, a novel and versatile intrusion detection system (IDS) for the optimal defense strategy is primarily introduced. Given the flexibility that wireless communication provides, it is unreasonable to expect malicious nodes will demonstrate a fixed behavior over time. Instead, malicious nodes can dynamically update the attack strategy in response to the IDS in each game stage. Thus, a multi-stage intrusion detection game (MIDG) based on Bayesian rules is proposed. In order to formulate the solution of MIDG, an in-depth analysis on the Bayesian equilibrium is performed iteratively. Depending on the MIDG theoretical analysis, the optimal behaviors of rational attackers and defenders are derived and calculated accurately. The numerical experimental results validate the effectiveness and robustness of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1442-1462
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• 2014

Packet classification is a key technology of the Internet for routers to classify the arriving packets into different flows according to the predefined rulesets. Previous packet classification algorithms have mainly focused on search speed and memory usage, while overlooking update performance. In this paper, we propose PreCuts, which can drastically improve the update speed. According to the characteristics of IP field, we implement three heuristics to build a 3-layer decision tree. In the first layer, we group the rules with the same highest byte of source and destination IP addresses. For the second layer, we cluster the rules which share the same IP prefix length. Finally, we use the heuristic of information entropy-based bit partition to choose some specific bits of IP prefix to split the ruleset into subsets. The heuristics of PreCuts will not introduce rule duplication and incremental update will not reduce the time and space performance. Using ClassBench, it is shown that compared with BRPS and EffiCuts, the proposed algorithm not only improves the time and space performance, but also greatly increases the update speed.

• 한국데이터정보과학회:학술대회논문집
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• 2005.10a
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• pp.59-69
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• 2005

Data mining technique is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. It has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on off-line or on-line and so on. We analyze Gyeongnam social indicator survey data by 2001 using twostep clustering technique for environment information. The twostep clustering is classified as a partitional clustering method. We can apply these twostep clustering outputs to environmental preservation and improvement.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.1-11
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• 2006

Data mining technique is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. It has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on off-line or on-line and so on. We analyze Gyeongnam social indicator survey data by 2001 using twostep clustering technique for environment information. The twostep clustering is classified as a partitional clustering method. We can apply these twostep clustering outputs to environmental preservation and improvement.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.93-98
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• 2018

The Internet of Things (IoT) is a form of the emerging 4th industry in the 5G era. IoT is expected to develop naturally in our daily life in the 5G era in which high-speed communication will be completed. Along with the rise of IoT, concerns about security and malicious attacks are also increasing. This paper examines DoS attacks, which are one of the representative security threats of IoT and proposes a local detection and blocking system that are suitable for response to such attacks. First, systems of the LoRaWAN type, which are most actively researched in the IoT system field and DoS attacks that can occur in such systems were examined. Then, the inverse order tree algorithm using regional characteristics was designed as a cluster analysis form. Finally, a system capable of defending denial-of-service attacks in the 5G IoT system using local detection and blocking with the Euclidean distance was designed.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.963-973
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• 2005

Increasing attention is being recently devoted to various problems inherent in the topological design of networks systems. The topological structure of these networks can be based on service centers, terminals (users), and connection cable. Lately, these network systems are well designed with tiber optic cable, because the requirements from users become increased. But considering the high cost of the fiber optic cable, it is more desirable that the network architecture is composed of a spanning tree. In this paper, we present a GA (Genetic Algorithm) for solving bicriteria network topology design problems of wide-band communication networks connected with fiber optic cable, considering the connection cost, average message delay, and the network reliability We also employ the $Pr\ddot{u}fer$ number (PN) and cluster string in order to represent chromosomes. Finally, we get some experiments in order to certify that the proposed GA is the more effective and efficient method in terms of the computation time as well as the Pareto optimality.