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�������պ����ѧѧ�� 2009, Vol. 35 Issue (5) :636-639    DOI:
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1. �������պ����ѧ ����������������ص�ʵ����,���� 100191;
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Adaptive aberrant network traffic detection algorithm based on time series forecast
Lü Junhui1, Zhou Gang2, Jin Yi1*
1. State Key Laboratory of Software Development Environment, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
2. National Digital Switching System Engineering & Technological Research Center, Zhengzhou, 450002, China

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Abstract�� The traditional network management tools usually detect aberrant network traffic according to the preset threshold. This method is straightforward, but it has poor adaptability. Therefore, A mature aberrant detection method called Holt-Winters based on the time series forecast was described. But it needed a long time to adapt the real network environment when the algorithm model applied. To solve these problems, based on the statistics of huge history network flow model, an increased Holt-Winter algorithm was proposed to calculate the base values and the model parameter values, which made the algorithm started faster. The result shows that the increased Holt-Winters algorithm has improved the detection accuracy and reduced the false alarm rate compared with threshold method and traditional Holt-Winters model.
Keywords�� aberration detection   time series analysis   Holt-Winters models     
Received 2008-08-10;


About author: ������(1983-),Ů,ɽ��������,˶ʿ��,lvjh@nlsde.buaa.edu.cn.
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L�� Junhui, Zhou Gang,Jin Yi.Adaptive aberrant network traffic detection algorithm based on time series forecast[J]  JOURNAL OF BEIJING UNIVERSITY OF AERONAUTICS AND A, 2009,V35(5): 636-639
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