A Comparative Study of Machine Learning and Deep Learning in Network Anomaly-Based Intrusion Detection Systems

Abstract

This paper presents a comparative study of Machine learning and Deep learning models used in anomaly-based network intrusion detection systems. The paper has presented an overview of the previous work done in the field of ML and DL IDS, then an overview of the used datasets in reviewed literature was presented. Moreover, ML and DL models were tested on the KDD-99 dataset, and performance results were presented, compared, and discussed. Finally, areas of future research of critical importance are proposed by the authors.