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Conference Paper Baseline Traffic Modeling for Anomalous Traffic Detection on Network Transit Points
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Authors
Yoo Hee Cho, Koo Hong Kang, Ik Kyun Kim, Ki Tae Jeong
Issue Date
2009-09
Citation
Asia-Pacific Network Operations and Management Symposium (APNOMS) 2009 (LNCS 5787), v.5787, pp.385-394
Publisher
Springer
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1007/978-3-642-04492-2_39
Project Code
09MS5300, Development of Anti-DDoS Technology, Jong Soo Jang
Abstract
Remarkable concerns have been made in recent years towards detecting the network traffic anomalies in order to protect our networks from the persistent threats of DDos and unknown attacks. As a pre-process for many state-of-the-art attack detection technologies, baseline traffic modeling is a prerequisite step to discriminate anomalous flow from normal traffic. In this paper, we analyze the traffic from various network transit points on ISP backbone network and present a baseline traffic model using simple linear regression for the imported NetFlow data; bits per second and flows per second. Our preliminary explorations indicate that the proposed modeling is very effective to recognize anomalous traffic on the real networks. © 2009 Springer Berlin Heidelberg.
KSP Keywords
Anomalous traffic detection, Attack Detection, Backbone Network, Netflow data, Network Traffic, Normal traffic, Real networks, Simple linear regression, Traffic anomalies, state-of-The-Art, traffic modeling