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Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey

Ali, Ali Hussein; Charfeddine, Maha; Ammar, Boudour; Hamed, Bassem Ben; Albalwy, Faisal; Alqarafi, Abdulrahman; Hussain, Amir

Authors

Ali Hussein Ali

Maha Charfeddine

Boudour Ammar

Bassem Ben Hamed

Faisal Albalwy

Abdulrahman Alqarafi



Abstract

The advancement of communication and internet technology has brought risks to network security. Thus, Intrusion Detection Systems (IDS) was developed to combat malicious network attacks. However, IDSs still struggle with accuracy, false alarms, and detecting new intrusions. Therefore, organizations are using Machine Learning (ML) and Deep Learning (DL) algorithms in IDS for more accurate attack detection. This paper provides an overview of IDS, including its classes and methods, the detected attacks as well as the dataset, metrics, and performance indicators used. A thorough examination of recent publications on IDS-based solutions is conducted, evaluating their strengths and weaknesses, as well as a discussion of their potential implications, research challenges, and new trends. We believe that this comprehensive review paper covers the most recent advances and developments in ML and DL-based IDS, and also facilitates future research into the potential of emerging Artificial Intelligence (AI) to address the growing complexity of cybersecurity challenges.

Citation

Ali, A. H., Charfeddine, M., Ammar, B., Hamed, B. B., Albalwy, F., Alqarafi, A., & Hussain, A. (2024). Unveiling machine learning strategies and considerations in intrusion detection systems: a comprehensive survey. Frontiers in Computer Science, 6, Article 1387354. https://doi.org/10.3389/fcomp.2024.1387354

Journal Article Type Article
Acceptance Date May 28, 2024
Online Publication Date Jun 10, 2024
Publication Date Jun 10, 2024
Deposit Date Aug 8, 2024
Publicly Available Date Aug 8, 2024
Journal Frontiers in Computer Science
Print ISSN 2624-9898
Electronic ISSN 2624-9898
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 6
Article Number 1387354
DOI https://doi.org/10.3389/fcomp.2024.1387354
Keywords network security, benchmark datasets, machine learning, deep learning, intrusion detection system

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.




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