Network Intrusion Detection System Using Voting Ensemble Machine Learning

Abstract

Due to increasing amount of cyber attack, there is a growing demand for Network intrusion detection systems (NIDSs) which are necessary for defending from potential attacks. Detecting and preventing cyber attacks is one of the key research areas. Existing NIDSs use traditional machine learning algorithms with low accuracy and are also not suitable for the new unknown cyber attacks. In this paper, we propose a NIDS model with ensemble machine learning methods. Ensemble machine learning methods have the potential to detect and prevent different types of attacks compared to traditional machine learning methods. Our proposed system can detect known attacks as well as can prevent unknown attacks. Our proposed system uses ensemble machine learning methods with Voting. We used the full NSL-KDD dataset to evaluate the performance of multiclass classification and we also compare the performance with deep learning as well as traditional base level machine learning techniques. Experimental results show that the proposed NIDS system is superior to the performance of existing methods. Our model improves the detection rate of the IDS which is vital for network intrusion detection systems.