• The Journal of the Korea Contents Association
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• v.2 no.3
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• pp.96-104
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• 2002

This paper has done a network performance test as well as result analysis of the test in a real campus gigabit network The inner network backbone consists of gigabit ethernet . The mean ratio of network use is below 1% age, and the maximum one comes to 10%. The floating capacity is still good enough to meet network flowing because the mean ratio of inner campus network as outer ones is 30.4% and the maximum one is merely 38.3%. But there appears a security problem. We must make a device blocking illegal approach to the client form outer network It is important that we can a away to manage the campus gigabit network efficiently on the basis of the given data from this performance test.

• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.130-139
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• 2007

As increasing the usefulness of wireless communication, there are developing the environment of ubiquitous that is combined the network of wireless and wire communication combinate type in various filed and in this network environment, u-campus constructions, based on ubiquitous sensor network, is developing. u-Campus is the new type campus which applies to ubiquitous computer technology and wireless network one. at this thesis, it is designed u-campus construction using zigbee technology with u-campus basic technology. u-Campus service, being subject to zigbee wireless communication skills, presents the members an convenient service using zigbee network.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.461-462
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• 2015

세계적으로 마이크로그리드에 이목이 집중되고 있는 현재 상황에서 DER(Distributed Energy Resources : 분산형 독립전원)의 역할이 대두되고 있다. 캠퍼스 마이크로그리드는 대학교의 캠퍼스를 하나이상의 DER로 선정하여 피크 시 교내 전기수요의 일부분을 해소할 수 있는 전력망 기반의 네트워크 시스템이다. 해외에서는 이미 캠퍼스 마이크로그리드 성공사례를 보여주었고, 이후 많은 캠퍼스에서 캠퍼스 마이크로그리드를 벤치마킹하고 있다. 본 논문에서는 캠퍼스 마이크로그리드에 필요한 DER을 구성하기 전 캠퍼스 내의 건물별, 계절별 전력수요 특성을 분석하고자 한다.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.231-234
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• 2010

지능형 캠퍼스는 정보통신 기술의 발전과 더불어 다양한 관련 기술이 활용되고 있다. 특히 유비쿼터스 컴퓨팅과 네트워크 기술을 대학의 캠퍼스에 접목시켜 구축한 새로운 형태의 캠퍼스가 지능형 캠퍼스이다. 본 논문에서는 물리적 공간, 사용자 행위, 디지털 환경 등을 고려하여 정보자원의 효율적 관리와 교수, 직원, 학생 등 사용자간 정보공유 편의성 증대를 위해 지리적으로 떨어진 교정을 통합한 모바일을 이용한 u-캠퍼스를 제안한다. u-캠퍼스는 단일 로그인으로 학생 포털, 교직원 포털, 일반 포털을 전체적으로 관리 지원하며 지능형 기반에서 모바일을 이용하여 실시간으로 사용된다.

• Proceedings of the Korea Society for Simulation Conference
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• 1998.03a
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• pp.21-26
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• 1998

본 연구는 Zeigler가 제시한 DEVS 모델링 및 시뮬레이션 방법론에 전문가 시스템 및 기호적 시뮬레이션기법등 다양한 추론기법들을 결합시킴에 의해, 캠퍼스 네트워크의 설계 및 성능분석 자동화 방법론과 도구의 개발을 주 목적으로 한다. 이를 위하여, 본 연구에서는 SES/MB를 이용하여 정보시스템의 구성설계 및 성능분석 자동화 방법론을 제시하였고, 캠퍼스 네트워크에 대한 사례 연구를 통하여 그 적용 가능성을 검토하였다. 기존의 연구들과의 차별성은 다음과 같이 요약된다; 첫째, 구성 전문가의 지식베이스를 활용한 구성 설계 자동화, 둘째, 해석적 기법이 아닌 시뮬레이션 기법을 이용한 성능분석, 셋째, 요구조전 및 제약조건들에 따른 행동궤적의 자동생성 및 이를 이용한 성능분석 자동화등을 들 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.379-380
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• 2022

KAIST 캠퍼스 기반의 실습환경 구축을 위하여 캠퍼스 전체를 스마트시티 테스트베드로 사용하며 CCTV 네트워크 기반 모니터링/관제 시스템 구축, 교통, 방범, 가로등, CCTV, 교내 버스 등 인프라 통합 관제 및 보안 실습실 구축하고 교내 자율주행 기술 연구진과 실습 협력 추진을 통한 캠퍼스 기반의 실전 스마트 환경을 토대로 다각도의 보안 공격/방어 실습을 진행하고 지자체 및 컨소시엄 기업들과 산학협력 프로젝트를 진행하기 위하여 구축한 내용을 설명한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.473-476
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• 2008

USN(Ubiquotous Sensor Network) has been applied to various fields of industries such as logistics, environment management, traffic management, as well as IT industries including home network and telematics. Among the important techniques required to implement aforementioned applications, location management scheme is essential. In this paper, we proposed and implemented a new location measurement scheme based on RSSI of sensor node for campus car location management.

• Journal of IKEEE
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• v.13 no.2
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• pp.140-149
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• 2009

Traffic anomaly detection is one of important technology that should be considered in network security and administration. In this paper, we propose an abnormal traffic detection mechanism that includes traffic monitoring and traffic analysis. We develop analytical passive monitoring system called WISE-Mon which can inspect traffic behavior. We establish a criterion by analyzing the characteristics of a traffic training set. To detect abnormal traffic, we derive a hyperplane by using Fisher linear discriminant and chi-square distribution as well as the analyzed characteristics of traffic. Our mechanism can support reliable results for traffic anomaly detection and is compatible to real-time detection. In addition, since the trend of traffic can be changed as time passes, the hyperplane has to be updated periodically to reflect the changes. Accordingly, we consider the self-learning algorithm which reflects the trend of the traffic and so enables to increase the pliability of detection probability. Numerical results are presented to validate the accuracy of proposed mechanism. It shows that the proposed mechanism is reliable and relevant for traffic anomaly detection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.33-41
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• 2018

The idea of the Internet of Things as a platform on the Smart Campus has become increasingly popular. It requires an infrastructure consisting of communication networks, sensor nodes and gateways to connect to the Internet. Each sensor node is responsible for gathering data from the environment. This document outlines a network of wireless sensors on the Internet for the application of Smart Campus monitoring. Wireless sensor network Monitoring have become a complete solution to using a low power implementation and integrated systems. The numerous restrictions however result from the low communication range, the limited computing power, the lack of availability of the network protocol, the lack of programming security and the security failures in the areas of confidentiality, integrity and availability. A new security technique and its functionality for WSNM nodes developed. Development in the research of a secure network and suggestions for avoiding denial of service (DOS) and complexity attacks. These systems if properly implemented can provide an energy efficiency mechanism through pre-allocation and a new key from key management models with a secure routine algorithm.

• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.283-294
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• 2006

Hostile network traffic is often different from normal traffic in ways that can be distinguished without knowing the exact nature of the attack. In this paper, we propose a new anomaly detection method using inbound network traffic distributions. For this purpose, we first characterize the traffic of a real campus network by the distributions of IP protocols, packet length, destination IP/port addresses, TTL value, TCP SYN packet, and fragment packet. And then we introduce the concept of entropy to transform the obtained baseline traffic distributions into manageable values. Finally, we can detect the anomalies by the difference of entropies between the current and baseline distributions. In particular, we apply the well-known denial-of-service attacks to a real campus network and show the experimental results.