Design of Multiple-Level Hybrid Classifier for Intrusion Detection System
Cheng Xiang, Sungsu Lim · 2006
As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. However, constructing and maintaining a misuse detection system is very labor-intensive since attack scenarios and patterns need to be analyzed and categorized, and the corresponding rules and patterns need to be carefully hand-coded. Thus, data mining can be used to ease this inconvenience. This paper proposes a multiple-level hybrid classifier, an intrusion detection system that uses a combination of tree classifiers and clustering algorithms to detect intrusions. Performance of this new algorithm is compared to other popular approaches such as MADAM ID and 3-level tree classifiers, and significant improvement has been achieved from the viewpoint of both high intrusion detection rate and reasonably low false alarm rate