Hybrid Intrusion Detection System Security Enrichment Using Classifier Ensemble

Abstract

Intrusion Detection Systems (IDS) is a software or device used to monitor a system or network for malicious activity. Thus, effective intrusion detection of different attacks. Existing methods of studies prove value of data mining methods in Intrusion Detection Systems (IDS). We focus on improving intrusion detection rate of IDS using Data Mining techniques. We implements a new classifier ensemble based intrusion detection systems (CEBIDS) using hybird detection approaches. CEBIDS combines feature level and data level techniques in WEKA tool with KDD cup’99 dataset enhances detection rate in significant manner.