Effective DDoS attacks detection using generalized entropy metric

Li, Ke, Zhou, Wanlei, Yu, Shui and Dai, Bo 2009, Effective DDoS attacks detection using generalized entropy metric, Lecture notes in computer science, vol. 5574, pp. 266-280.

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Title Effective DDoS attacks detection using generalized entropy metric
Author(s) Li, Ke
Zhou, Wanlei
Yu, Shui
Dai, Bo
Journal name Lecture notes in computer science
Volume number 5574
Start page 266
End page 280
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2009-07-31
ISSN 0302-9743
1611-3349
Keyword(s) DDoS
generalized entropy
attacks detection
Summary In information theory, entropies make up of the basis for distance and divergence measures among various probability densities. In this paper we propose a novel metric to detect DDoS attacks in networks by using the function of order α of the generalized (Rényi) entropy to distinguish DDoS attacks traffic from legitimate network traffic effectively. Our proposed approach can not only detect DDoS attacks early (it can detect attacks one hop earlier than using the Shannon metric while order α=2, and two hops earlier to detect attacks while order α=10.) but also reduce both the false positive rate and the false negative rate clearly compared with the traditional Shannon entropy metric approach.
Language eng
Field of Research 080501 Distributed and Grid Systems
Socio Economic Objective 890101 Fixed Line Data Networks and Services
HERDC Research category C2 Other contribution to refereed journal
Copyright notice ©2009, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30029188

Document type: Journal Article
Collection: School of Information Technology
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Created: Wed, 09 Jun 2010, 12:27:55 EST by Linda Aldridge

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