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 …

With increasing connectivity between computers, the need to keep networks secure
progressively becomes more vital. Intrusion detection systems (IDS) have become an
essential component of computer security to supplement existing defenses. This paper
proposes a multiple-level hybrid classifier, a novel intrusion detection system, which
combines the supervised tree classifiers and unsupervised Bayesian clustering to detect
intrusions. Performance of this new approach is measured using the KDDCUP99 dataset …