Ensembling Rule Based Classifiers for Detecting Network Intrusions

An intrusion is defined as a violation of the security policy of the system, and hence, intrusion detection mainly refers to the mechanisms that are developed to detect violations of system security policy. Recently, data mining techniques have gained importance in providing the valuable information which in turn can help to enhance the decision on identifying the intrusions (attacks). In this paper; we evaluate the performance of various rule based classifiers like: JRip, RIDOR, NNge and decision table using ensemble approach in order to build an efficient network intrusion detection system. We use KDDCup´99, intrusion detection benchmark dataset (which is a part of DARPA evaluation program) for our experimentation. It can be observed from the results that the proposed approach is accurate in detecting network intrusions, provides low false positive rate, simple, reliable and faster in building an efficient network intrusion system.