Optimizing Network Intrusion Detection Using Machine Learning

Abstract

Machine learning (ML) techniques are essential in the detection of network attacks and enhancing network security. A device or software that recognizes any unusual pattern in the network and alerts the user about the same is an intrusion detection system (IDS). In this chapter, we have described the use of ML classification algorithms on the UNSW-NB15 dataset, leading to the generation of a network intrusion detection model which classifies incoming traffic into malicious or non-malicious traffic and issues an alert to the user. We have implemented the following ML algorithms – support vector machine, artificial neural network, and one-class support vector machine with an average accuracy of 89.25%, 91.54%, and 93.05%, respectively. Two graphical user interfaces (online and offline versions) have been developed for the system. Thus, the chapter proposes an optimized intrusion detection system that improves upon the existing intrusion detection systems which detect malicious packets in the network.