Compiling network traffic into rules using soft computing methods for the detection of flooding attacks

Authors:
Sanguk Noh;Gihyun Jung;Kyunghee Choi;Cheolho Lee
Affiliations:
School of Computer Science and Information Engineering, The Catholic University of Korea, Bucheon, Republic of Korea;Division of Electronics Engineering, Ajou University, Suwon, Republic of Korea;Graduate School of Information and Communication, Ajou University, Suwon, Republic of Korea;National Security Research Institute, Daejeon, Republic of Korea
Venue:
Applied Soft Computing
Year:
2008

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Abstract

The ability to dynamically collect and analyze network traffic and to accurately report the current network status is critical in the face of large-scale intrusions, and enables networks to continually function despite of traffic fluctuations. The paper presents a network traffic model that represents a specific network pattern and a methodology that compiles the network traffic into a set of rules using soft computing methods. This methodology based upon the network traffic model can be used to detect large-scale flooding attacks, for example, a distributed denial-of-service (DDoS) attack. We report experimental results that demonstrate the distinctive and predictive patterns of flooding attacks in simulated network settings, and show the potential of soft computing methods for the successful detection of large-scale flooding attacks.