Network Intrusion Detection Using Machine Learning Techniques

Abstract

Intrusion detection systems (IDS) are essential for the protection of advanced communication networks. These systems were primarily designed to identify particular patterns, signatures, and rule violations. Machine Learning and Deep Learning approaches have been used in recent years in the field of network intrusion detection to provide promising alternatives. These approaches can discriminate between normal and anomalous patterns. In this paper, the NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) benchmark data set has been used to evaluate Network Intrusion Detection Systems (NIDS) by using different machine learning algorithms such as Support Vector Machine, J48, Random Forest, and Naïve Bytes with both binary and multi-class classification. The results of the application of those techniques are discussed in details and outperformed previous works.