Network Intrusion Detection using Machine Learning Approaches

Abstract

At present network intrusion is regarded as a great threat in network usage and communication. Network intrusion detection system detects and prevents anomalous activities or attacks in networks. Many classifiers are used to detect network attacks. In this paper, we have evaluated the performance of four popular classifiers, namely, Decision Tree, Support Vector Machine, Random Forest and Naïve Bayes on UNSW-NB15 dataset using Python language along with its Pandas and SKlearn libraries. We have used the complete UNSW-NB15 dataset with 43 features. Experimental results have shown improvement of accuracy for Random Forest, Decision Tree and Naïve Bayes over previously reported results produced by Apache Spark and its MLlib.