The role of machine and deep learning in modern intrusion detection systems: A comprehensive review

Abstract

Network intrusion benchmark datasets serve an essential role in improving the advancement of research in cybersecurity because they provide standardized resources for assessing the effectiveness of intrusion detection systems and associated cybersecurity solutions. This review article provides a detailed examination of the cutting-edge in network intrusion benchmark datasets, concentrating on their features, content, utilization, and implications for cybersecurity research. We systematically review a wide variety of benchmark datasets that are often utilized in the industry, which include the DARPA, KDDcup99, NSL-KDD, Kyoto, UNSW-NB15, and CICIDS-17 datasets. We analyzed each dataset, including its performance based on machine learning and deep learning models, by critically synthesizing existing literature. Additionally, we discussed the common challenges existing in intrusion detection systems. Furthermore, we provided a description of various machine learning and deep learning algorithms used for intrusion detection applications. This study aims to assist researchers in choosing suitable datasets and techniques for evaluating and benchmarking intrusion detection systems, ultimately advancing cybersecurity research and the development of reliable and efficient cybersecurity solutions.