Chaos theory based detection against network mimicking DDoS attacks

Authors:
Ashley Chonka;Jaipal Singh;Wanlei Zhou
Affiliations:
School of Engineering and Information Technology, Deakin University, Melbourne, Australia;Digital Ecosystems and Business Intelligence Institute, Curtin University, Perth, Australia;School of Engineering and Information Technology, Deakin University, Melbourne, Australia
Venue:
IEEE Communications Letters
Year:
2009

Citing 3
Cited 3

Self-Similar Network Traffic and Performance Evaluation

Self-Similar Network Traffic and Performance Evaluation
On the relationship between file sizes, transport protocols, and self-similar network traffic

ICNP '96 Proceedings of the 1996 International Conference on Network Protocols (ICNP '96)
Denial-of-Service Attack-Detection Techniques

IEEE Internet Computing

Cloud security defence to protect cloud computing against HTTP-DoS and XML-DoS attacks

Journal of Network and Computer Applications
A confidence-based filtering method for DDoS attack defense in cloud environment

Future Generation Computer Systems
Detecting denial of service by modelling web-server behaviour

Computers and Electrical Engineering

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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.