Ensemble of One-Class Classifiers for Network Intrusion Detection System

To achieve high accuracy while lowering false alarm rates are major challenges in designing an intrusion detection system. In addressing this issue, this paper proposes an ensemble of one-class classifiers where each uses different learning paradigms. The techniques deployed in this ensemble model are; linear genetic programming (LGP), adaptive neural fuzzy inference system (ANFIS) and random forest (RF). The strengths from the individual models were evaluated and ensemble rule was formulated. Empirical results show an improvement in detection accuracy for all classes of network traffic; normal, probe, DoS, U2R and R2L. RF, which is an ensemble learning technique that generates many classification trees and aggregates the individual result was also able to address imbalance dataset problem that many of machine learning techniques fail to sufficiently address it.