Abstract
In a large backbone network, it is important to detect shape traffic fluctuation for servicing robust network. However, there are too many interfaces to monitor the characteristics of traffic. First we collect volume traffic of boundary link. From the volume traffic, we make groups which have similar traffic patterns by hierarchical clustering algorithm. This result shows that most of traffic has similar patterns, but some traffic which is far from centroid has an anomaly traffic pattern. This paper gives a hint for network operators that which traffic has to be checked out.
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Son, C., Cho, SH., Yoo, JH. (2009). Volume Traffic Anomaly Detection Using Hierarchical Clustering. In: Hong, C.S., Tonouchi, T., Ma, Y., Chao, CS. (eds) Management Enabling the Future Internet for Changing Business and New Computing Services. APNOMS 2009. Lecture Notes in Computer Science, vol 5787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04492-2_30
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DOI: https://doi.org/10.1007/978-3-642-04492-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04491-5
Online ISBN: 978-3-642-04492-2
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