BROWSE
Detail
Conference Paper
A Combined Data Mining Approach for DDoS Attack Detection
Cited 24 time in 
Share 
Share
- Authors
- Mi Hui Kim, Hyun Jung Na, Ki Joon Chae, Hyo Chan Bang, Jung Chan Na
- Issue Date
- 2004-11
- Citation
- International Conference on Information Networking (ICOIN) 2004 (LNCS 3090), v.3090, pp.943-950
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not provide effective defense against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. It is necessary to analyze the fundamental features of DDoS attacks because these attacks can easily vary the used port/protocol, or operation method. In this paper, we propose a combined data mining approach for modeling the traffic pattern of normal and diverse attacks. This approach uses the automatic feature selection mechanism for selecting the important attributes. And the classifier is built with the theoretically selected attribute through the neural network. And then, our experimental results show that our approach can provide the best performance on the real network, in comparison with that by heuristic feature selection and any other single data mining approaches. © Springer-Verlag Berlin Heidelberg 2004.
- KSP Keywords
- Automatic feature selection, Best performance, DDoS attack detection, Data mining(DM), Defense Capability, Feature selection(FS), Operation method, Rapid detection, Selection mechanism, Traffic pattern, data mining approach