Improving the accuracy of intrusion detection systems by using the combination of machine learning approaches

An Intrusion detection system is designed to classify the system activities into normal and abnormal. We use a combination of machine learning approaches as to detect the system attacks. The experimental results of the study show that increasing the number of classifiers has a threshold limit and the system accuracy will remain constant if the number of classifiers goes beyond this limit. The determination of the threshold limit is tentative. This article, also, presents a solution for unbalanced data of some attacks. The comparison of the results with other similar articles proves the efficiency of the presented system.