Intrusion detection model based on ensemble learning for U2R and R2L attacks

Abstract

Intrusion Detection System (IDS) is a tool for anomaly detection in network that can help to protect network security. At present, intrusion detection systems have been developed to prevent attacks with accuracy. In this paper, we concentrate on ensemble learning for detecting network intrusion data, which are difficult to detect. In addition, correlation-based algorithm is used for reducing some redundant features. Adaboost algorithm is adopted to create the ensemble of weak learners in order to create the model that can protect the security and improve the performance of classifiers. The U2R and R2L attacks in KDD Cup'99 intrusion detection dataset are used to train and test the ensemble classifiers. The experimental results show that reducing features can improve efficiency in attack detection of classifiers in many weak leaners.