Unveiling the Landscape of Machine Learning and Deep Learning Methodologies in Network Security: A Comprehensive Literature Review

Nouf Majid Sultan Eid Saeed, Amer Ibrahim, Liaqat Ali, Nidal A. Al-Dmour, Abdul Salam Mohammed, Taher M. Ghazal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The dynamic nature of cyber threats offers a continual problem in the field of cybersecurity in the context of the expanding internet environment. This study provides an in-depth assessment of the literature on machine learning (ML) and deep learning (DL) methodologies for network analysis for intrusion detection. This review curates, assesses, and distils method-specific findings while considering temporal or thermal correlations. It provides a recognition of the importance of data in ML and DL approaches, and a comprehensive overview of frequently used network datasets in ML/DL applications, as well as the inherent challenges of adopting ML/DL in the cybersecurity field. The study concludes with well-informed recommendations for future areas of research in this critical domine.

Original languageBritish English
Title of host publication2nd International Conference on Cyber Resilience, ICCR 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350394962
DOIs
StatePublished - 2024
Event2nd International Conference on Cyber Resilience, ICCR 2024 - Dubai, United Arab Emirates
Duration: 26 Feb 202428 Feb 2024

Publication series

Name2nd International Conference on Cyber Resilience, ICCR 2024

Conference

Conference2nd International Conference on Cyber Resilience, ICCR 2024
Country/TerritoryUnited Arab Emirates
CityDubai
Period26/02/2428/02/24

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