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Chaos theory based detection against network mimicking DDoS attacks

Chonka, Ashley and Zhou, Wanlei 2009, Chaos theory based detection against network mimicking DDoS attacks, IEEE communications letters, vol. 13, no. 9, pp. 717-719.

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Title Chaos theory based detection against network mimicking DDoS attacks
Author(s) Chonka, Ashley
Zhou, Wanlei
Journal name IEEE communications letters
Volume number 13
Issue number 9
Start page 717
End page 719
Total pages 3
Publisher IEEE
Place of publication Piscataway, NJ
Publication date 2009-09
ISSN 1089-7798
1558-2558
Keyword(s) Distributed denial-of-service (DDoS)
Anomaly detection
Chaotic models
Summary DDoS attack traffic is difficult to differentiate from legitimate network traffic during transit from the attacker, or zombies, to the victim. In this paper, we use the theory of network self-similarity to differentiate DDoS flooding attack traffic from legitimate self-similar traffic in the network. We observed that DDoS traffic causes a strange attractor to develop in the pattern of network traffic. From this observation, we developed a neural network detector trained by our DDoS prediction algorithm. Our preliminary experiments and analysis indicate that our proposed chaotic model can accurately and effectively detect DDoS attack traffic. Our approach has the potential to not only detect attack traffic during transit, but to also filter it.
Language eng
Field of Research 080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2009
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30028921

Document type: Journal Article
Collection: School of Information Technology
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Created: Wed, 26 May 2010, 18:56:31 EST by Sandra Dunoon