Ensemble classifiers for supervised anomaly based network intrusion detection

Abstract

This paper focuses on the problem of machine learning classifier choice for network intrusion detection, taking into consideration several ensemble classifiers from the supervised learning category. We have evaluated Bagged trees, AdaBoost, RUSBoost, LogitBoost and GentleBoost algorithms, provided an analysis of the performance of the classifiers and compared their learning capabilities, taking for the reference UNSW-NB15 dataset. The obtained results have indicated that in the defined environment and under analyzed conditions Bagged tree and GentleBoost perform with highest accuracy and ROC values, while RUSBoost has the lowest performances.