An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers

Abstract

With the rapid growth and advancement in technology, we are becoming more and more dependent on the internet for our day-to-day work. As a result of this, we have become an easy target for the attackers. More the usage of the internet more we are vulnerable to threats. In this scenario, the need for anti-virus or some system that can help in detecting the threats or attacks is also growing rapidly. The answer to this problem is a system that when installed on a network shall be able to detect Intrusions of any sort. Such a system can be called an IDS (Intrusion Detection System). This system is deployed on any network to keep a track of the traffic and to monitor any kind of mutation or deviations from regular traffic patterns. Many kinds of research are still ongoing in this field to develop a system that has not only a better error detection rate but also can introduce preventive measures as soon as a threat is detected. This paper proposes a system for detecting the attacks using machine learning where the Recursive Feature Elimination (RFE) technique is applied when the irrelevant features are there in the dataset. This technique helps remove any sort of redundancy in the KDD CUP99 dataset which is a standard dataset for network security and intrusion detection. Then for the achieved set of features a confusion matrix is generated and the classification of records is done using decision tree, support vector machine, and an ensemble classifier random forest in the form of discriminant analysis. When compared with other methodologies our approach holds a good classification rate for all the classes of attacks of the KDD CUP 99 dataset.