Machine Learning-Based and Deep Learning-Based Intrusion Detection System: A Systematic Review

The network size and corresponding data have grown dramatically as a result of rapid advancements in the internet and communication fields. The corresponding growth in new attacks has made it challenging for network security to accurately identify breaches. The rising complexity and severity of security assaults on computer networks has prompted security experts to develop new strategies for protecting organization’s data and reputation. In order to improve the effectiveness of intrusion detection systems (IDS) in protecting computer networks and hosts, Machine Learning and Deep Learning have become more popular methodologies. This paper presents a thorough analysis and proposal of intrusion detection systems that are based on machine learning and deep learning. It initially describes the essential principles of the Intrusion Detection System (IDS) and the research methods for this paper. Also mention different machine learning and deep learning approaches of Intrusion Detection System (IDS). It then groups these schemes based on the kinds of techniques that are used in each of them. Concluding observations and future prospects are highlighted, and a thorough analysis of the examined Intrusion Detection System (IDS) frameworks is addressed.