• Proceedings of the Korean Information Science Society Conference
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• 2003.10c
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• pp.565-567
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• 2003

액티브 네트워크 기술은 스위치나 라우터들이 패킷의 단순한 경로 배정이나 전달만이 아닌 사용자에 의해 장착된 프로그램이나 프로그램 코드를 담은 액티브 패킷에 의해 데이터를 처리할 수 있도록 한 것이다. 본 논문에서는 기존의 네트워크 환경의 중앙 집중적인 관리 시스템의 문제점을 해결하고 좀 더 효율적인 네트워크 관리를 위해 차세대 인터넷 기술인 액티브 네트워크 기술을 적용한 네트워크 관리 시스템을 설계하였다. 특히, 네트워크 관리 환경내의 라우터들에게 관리 정보들을 패턴화 하여 처리하게 하여 트래픽을 줄이고. 자신이 속한 LAN 환경내의 관리 대상들을 분산화 처리할 수 있는 The Smart Router 모델을 제안하였다.

• The KIPS Transactions:PartC
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• v.12C no.1 s.97
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• pp.19-28
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• 2005

Recently, it has been sharply increased the interests to detect the network traffic anomalies to help protect the computer network from unknown attacks. In this paper, we propose a new anomaly detection scheme using the simple linear regression analysis for the exported LetFlow data, such as bits per second and flows per second, from a border router at a campus network. In order to verify the proposed scheme, we apply it to a real campus network and compare the results with the Holt-Winters seasonal algorithm. In particular, we integrate it into the RRDtooi for detecting the anomalies in real time.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.1-10
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• 2010

As recent fusion industry and ubiquitous technology have grown fast, network contents, which require high load, are provided in various infrastructures and facilities such as u-city and smart phones. Therefore, it is anticipated that the playback quality of multimedia compared to network loads degrades dramatically due to the drastic increment of real-time reference of conventional high load contents (eg. multimedia data). In this paper, we improved the method of the traffic assignment based on MPP which elevated the playback quality of multimedia by assigning discriminately the possible traffic of MMS with TP coefficients. When the TP coefficient which combines content preference with media preference was applied to a real-time traffic assignment scheduler, the simulation results showed that the multimedia playback stream was assigned within the possible traffic of a server. The real-time scheduling algorithm was improved by using the TP coefficient that combines the time-dependent image contents and the weighted value of media preference. It was observed from the experiment that the loss of the possible traffic decreases to 3.91% and 3.88% for three and four clients respectively.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.185-188
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• 2013

음성통화 위주의 통신 시장이 무선 인터넷 중심으로 변함에 따라 스마트디바이스의 생태계가 진화하고 있다. 일반 데스크탑 환경에서 보여지는 웹을 바탕으로 모바일 디바이스에서도 데스크탑 퀄리티의 환경을 사용할 수 있게 되었다. 또한 UI를 비롯하여 사용자 중심의 각종 서비스들이 개발되고 있으며 향후, 2016년까지 전 세계 모바일 데이터 트래픽이 현재의 10배 수준으로 증가할 것으로 전망하고 있다. 본 논문에서는 기존 모바일디바이스를 활용한 주문 시스템의 단점과 한계를 APNs방식을 사용하여 모바일 데이터사용량을 최소화함으로써 모바일 데이터 트래픽의 낭비가 심한 문제점을 보완하였다. 또한 TLS peer-to-peer방식을 사용하여 신뢰성 있는 접속환경을 만듦으로써 보완성을 높였다. 전송에 대한 반응과 보완성을 높여 기존 방식에 비하여 더욱 안정적인 환경을 구축하였다.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.649-658
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• 2005

Since the Network based attack Is extensive in the real state of damage, It is very important to detect intrusion quickly at the beginning. But the intrusion detection using supervised learning needs either the preprocessing enormous data or the manager's analysis. Also it has two difficulties to detect abnormal traffic that the manager's analysis might be incorrect and would miss the real time detection. In this paper, we propose a traffic attributes correlation analysis mechanism based on self-organizing maps(SOM) for the real-time intrusion detection. The proposed mechanism has three steps. First, with unsupervised learning build a map cluster composed of similar traffic. Second, label each map cluster to divide the map into normal traffic and abnormal traffic. In this step there is a rule which is created through the correlation analysis with SOM. At last, the mechanism would the process real-time detecting and updating gradually. During a lot of experiments the proposed mechanism has good performance in real-time intrusion to combine of unsupervised learning and supervised learning than that of supervised learning.

• Journal of IKEEE
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• v.10 no.2 s.19
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• pp.123-127
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• 2006

An Ad-hoc network which is composed of mobile nodes only should be able to distribute the traffic load evenly to the network so that the network could utilize its resources effectively However, most of researches have been concentrated in channel establishment level, not in channel reestablishment level. In this paper, the existing AODV routing protocol has been modified and upgraded to disperse the traffic load more quickly A node which uses the modified AODV routing protocol proposed in this paper broadcasts a control message to adjacent nodes, which announces congestion around the node. In order to verify the performance improvement of the proposed protocol, simulation study has been carried out by using ns2 simulator.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.115-121
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• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.8
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• pp.1498-1507
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• 2017

According to recent report, global mobile data traffic is expected to increase by 11 times from 2016 to 2020. Moreover, this growth is expected to be driven mainly by mobile video traffic which is expected to account for about 70% of the total mobile data traffic. To cope with enormous mobile traffic, we need to understand video traffic's characteristic. Recently, the repetitive requests of some popular content such as popular YouTube videos cause a enormous network traffic overheads. If we constitute a network with the nodes capable of content caching based on the content popularity, we can reduce the network overheads by using the cached content for every request. Through device-to-device, multicast, and helpers, the video throughput can improve about 1.5~2 times and prefix caching reduces the playback delay by about 0.2~0.5 times than the conventional method. In this paper, we introduce some recent work on content popularity-based caching techniques in wireless networks.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10c
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• pp.601-603
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• 2004

유비쿼터스 환경은 사용자가 컴퓨터나 네트워크를 의식하지 않으며 장소에 구애받지 않는 컴퓨팅 환경을 제시하고 있어 사용자는 스케줄러를 항상 소지하지 않고도 컴퓨터에 접속하여 일정을 관리할 수 있다. 그러나 그러한 환경에서는 일정관리 데이터가 불일치한 채로 분산되어 관리되는 경우가 빈번히 발생하며 그러한 문제를 해결하기 위한 데이터 동기화 기법이 필요하다 기존의 데이터 동기화 기법들은 클라이언트-서버 구조를 가지고 있어 서버에 트래픽이 집중되는 단점이 있다. 본 논문에서는 이동 에이전트를 이용하여 트래픽을 특정 서버에 집중시키지 않고 일정관리 데이터를 동기화하는 기법을 제안한다. 본 제안 기법을 적용함으로써 일정관리 데이터뿐만 아니라 다망한 데이터의 동기화를 수행할 수 있을 것이다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.203-211
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• 2006

Today, Internet covers a world wide range and most appliances of our life are linked to network from enterprise server to household electric appliance. Therefore, the importances of administrable framework that can grasp network state by real-time is increasing day by day. Our objective in this paper is to describe a network weather report framework that monitors network traffic and performance state to report a network situation including traffic status in real-time. We also describe a mobile agent architecture that collects state information in each network segment. The framework could inform a network manager of the network situation. Through the framework. network manager accumulates network data and increases network operating efficiency.