NDLI: Traffic dispersion graph based anomaly detection

Content Provider	ACM Digital Library
Author	Le, Do Quoc Hong, James Won-Ki Roman, H. Eduardo Jeong, Taeyoel
Abstract	Detecting and diagnosing anomalous traffic are important aspects of managing IP networks. In this paper, we propose a novel approach to detect anomalous network traffic based on graph theory concepts such as degree distribution, maximum degree and dK-2 distance. In this approach, we have used the traffic dispersion graphs (TDG) to model network traffic over time. We analyze differences of TDG graphs in time series to detect anomalies and introduce a method to identify attack patterns in anomalous traffic. The approach has been validated by using network traces from POSTECH and CAIDA.
Starting Page	36
Ending Page	41
Page Count	6
File Format	PDF
ISBN	9781450308809
DOI	10.1145/2069216.2069227
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Traffic dispersion graphs Anomalous traffic detection Network security Ddos attacks Network monitoring and analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Traffic dispersion graph based anomaly detection