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dc.contributor.authorChonka, A.
dc.contributor.authorSingh, Jaipal
dc.contributor.authorZhou, W.
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.

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.titleIEEE Communication Letters

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

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