• Title/Summary/Keyword: Traffic Dispersion Graphs

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An Anomalous Host Detection Technique using Traffic Dispersion Graphs (트래픽 분산 그래프를 이용한 이상 호스트 탐지 기법)

  • Kim, Jung-Hyun;Won, You-Jip;Ahn, Soo-Han
    • Journal of KIISE:Information Networking
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    • v.36 no.2
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    • pp.69-79
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    • 2009
  • Today's Internet is one of the necessaries of our life. Anomalies of the Internet provoke social problems. For that reason, Internet Measurement which studies characteristics on Internet traffic attracts pubic attention. Recently, Traffic Dispersion Graph (TDG), a novel traffic analysis method, was proposed. The TDG is not a statistical analysis method but a graphical visualization method on interactions among network components. In this paper, we propose a new anomaly detection paradigm and its technique using TDG. The existing studies have focused on detecting anomalous packets of flows. On the other hand, we focus on detecting the sources of anomalous traffic. To realize our paradigm, we designed the TDG Clustering method. Through this method, we could classify anomalous hosts infected by various worm viruses. We obtained normal traffic through dropping traffic of the anomalous hosts. Especially, we expect that the TDG clustering method can be applied to real-time anomaly detection because calculations of the method are fast.

Analysis on the Korean Highway in 2011 and 2017 Using Algorithms of Accessibility indices (접근성 지표의 알고리즘을 이용한 2011년과 2017년의 우리나라 고속도로 분석)

  • Lee, Gwangyeon;Park, Kisoeb
    • Journal of the Korea Society for Simulation
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    • v.27 no.4
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    • pp.9-18
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    • 2018
  • This paper proposes new algorithms of accessibility indices to analyze the connectivity of the Korean highway network. First of all, we find a transportation network that presents Korea's highway network in graphs in 2011 and 2017. And we analyze and compare the nation's highway network in 2011 and 2017 using concepts such as associated number, the relative distance, the accessibility, the degree of connectivity, the index of dispersion, the diameter of graph theory. To do this, an algorithm is presented which can easily obtain various accessibility indices from a given transportation network. Using the simulation results of this study, we can find city that is the center of traffic in the highway transportation network. In addition, cities that are included in the network but are relatively underdeveloped can be found and used as basic data for enhancing the connectivity of the nationwide traffic in the future. Moreover, the proposed algorithms of accessibility indices, which are modeled on highway transport networks, can help identify the accessibility space structure of each city and provide criteria for efficient and reasonable selection of alternatives in various regional planning processes, including transportation.