Classifier Ensemble Design with Rotation Forest to Enhance Attack Detection of IDS in Wireless Network

This paper is devoted to discover the appropriate base classifier algorithms while employing Rotation Forest as an ensemble learning method for intrusion detection system (IDS) in wireless network. Twenty different classification algorithms are involved in the experiment and their detection performances are assessed using the value of area under receiver operating characteristic curve (AUC) performance metric. The performance result of an ensemble learner are evaluated, including its significant improvement while using diverse machine leaning algorithms as base classifiers. From the experimental result and classifier significant test, it can be revealed that the performance of Rotation Forest has brought significant improvement over the base classifiers.