Show simple item record

dc.contributor.authorChonka, A.
dc.contributor.authorSingh, Jaipal
dc.contributor.authorZhou, W.
dc.date.accessioned2017-01-30T10:45:21Z
dc.date.available2017-01-30T10:45:21Z
dc.date.created2010-02-21T20:02:44Z
dc.date.issued2009
dc.identifier.citationChonka, Ashley and Singh, Jaipal and Zhou, Wanlei. 2009. Chaos Theory Based Detection against Network Mimicking DDoS Attacks. IEEE Communication Letters. 13 (9): pp. 717-719.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/5327
dc.description.abstract

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.

dc.publisherIEEE Communications Society
dc.subjectanomaly detection
dc.subjectchaotic models
dc.subjectDistributed denial-of-service (DDoS)
dc.titleChaos Theory Based Detection against Network Mimicking DDoS Attacks
dc.typeJournal Article
dcterms.source.volume13
dcterms.source.number9
dcterms.source.startPage717
dcterms.source.endPage719
dcterms.source.issn1089-7798
dcterms.source.titleIEEE Communication Letters
curtin.note

Copyright © 2009 IEEE 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.

curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access
curtin.facultyCurtin Business School
curtin.facultyThe Digital Ecosystems and Business Intelligence Institute (DEBII)


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record