Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A comprehensive review of AI based intrusion detection system Subject The topic of the resource Artificial intelligence; Deep learning; Ensemble learning; Intrusion detection system; Machine learning Description An account of the resource In today's digital world, the tremendous amount of data poses a significant challenge to cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to detect them. Signature-based intrusion detection has been the common method used for detecting attacks and providing security. However, with the emergence of Artificial Intelligence (AI), particularly Machine Learning, Deep Learning and ensemble learning, promising results have been shown in detecting attacks more efficiently. This review discusses how AI-based mechanisms are being used to detect attacks effectively based on relevant research. To provide a broader view, the study presents taxonomy of the existing literature on Machine Learning (ML), Deep learning (DL), and ensemble learning. The analysis includes 72 research papers and considers factors such as the algorithm and performance metrics used for detection. The study reveals that AI-based intrusion detection methods improve accuracy, but researchers have primarily focused on improving performance for detecting attacks rather than individual attack classification. The main objective of the study is to provide an overview of different AI-based mechanisms in intrusion detection and offer deeper insights for future researchers to better understand the challenges of multi-classification of attacks. 2023 Creator An entity primarily responsible for making the resource Sowmya T.; Mary Anita E.A. Source A related resource from which the described resource is derived Measurement: Sensors, Vol-28 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.measen.2023.100827" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.measen.2023.100827</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163471347&amp;doi=10.1016%2Fj.measen.2023.100827&amp;partnerID=40&amp;md5=38c5ff8dc0c55fc5ce68b9a51dca1a02" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163471347&amp;doi=10.1016%2fj.measen.2023.100827&amp;partnerID=40&amp;md5=38c5ff8dc0c55fc5ce68b9a51dca1a02</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 26659174 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sowmya T., Department of CSE, Christ University Bangalore, CMR Institute of Technology, Bengaluru, India; Mary Anita E.A., Department of CSE, Christ University, Bangalore, India