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Chaos Theory Based Detection against Network Mimicking DDoS Attacks

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Open access
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Date
2009
Type
Journal Article

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Chonka, 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.
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IEEE Communication Letters
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Curtin Business School
The Digital Ecosystems and Business Intelligence Institute (DEBII)
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Digital Ecosystems and Business Intelligence Institute (DEBII)
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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.