Intrusion detection using neural based hybrid classification methods
Data mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in databases process. Classification is a very common data mining task. Classification maps data into predefined groups or classes. It is often referred to as supervised learning because the classes are determined before examining the data. Due to increasing incidents of cyber attacks, building effective intrusion detection systems are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. This paper presents two classification methods involving multilayer perceptron and radial basis function and an ensemble of multilayer perceptron and radial basis function. We propose hybrid architecture involving ensemble and base classifiers for intrusion detection systems. The analysis of results shows that the performance of the proposed method is superior to that of single usage of existing classification methods such as multilayer perceptron and radial basis function. Additionally it has been found that ensemble of multilayer perceptron is superior to ensemble of radial basis function classifier for normal behavior and reverse is the case for abnormal behavior. We show that the proposed method provides significant improvement of prediction accuracy in intrusion detection.
intrusion detection classification data mining ensemble hybrid architecture