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Chaos theory based detection against network mimicking DDoS attacks

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journal contribution
posted on 2009-09-01, 00:00 authored by Ashley Chonka, Wanlei Zhou
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.

History

Journal

IEEE communications letters

Volume

13

Issue

9

Pagination

717 - 719

Publisher

IEEE

Location

Piscataway, NJ

ISSN

1089-7798

eISSN

1558-2558

Language

eng

Notes

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2009, IEEE

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