Please use this identifier to cite or link to this item:
Title: The Intrusion Detection System by Deep Learning Methods: Issues and Challenges
Authors: Surakhi, Ola 
García, Antonio 
Jamoos, Mohammed 
Alkhanafseh, Mohammad 
Keywords: Artificial intelligence;Data mining;Information retrieval;Deep learning (Machine learning);Intrusion detection systems (Computer security);Machine learning;Computer security;Artificial intelligence - Security measures
Issue Date: 2022
Publisher: International Arab Journal of Information Technology
Abstract: Intrusion Detection Systems (IDS) are one of the major research application problems in the computer security domain. With the increasing number of advanced network attacks, the improvement of the traditional IDS techniques become a challenge. Efficient ways and methods of identifying, protecting, and analyzing data are needed. In this paper, a comprehensive survey on the application of Machine Learning (ML) and Deep Learning (DL) methods on the IDS to increase detection accuracy and reduce error rate is proposed. The recent research papers that have been published between 2018 and 2021 in the area of applying ML and DL in the IDS are analyzed and summarized. Four main analyzing aspects are presented as follows: (1) IDS concepts and taxonomy. (2) The strength and weaknesses of ML and DL methods. (3) IDS benchmark datasets. (4) Comprehensive review of the most recent articles that used ML and DL to improve IDS with highlighting the strengths and weaknesses of each work. Based on the analysis of the literature review papers, a framework for the application of ML and DL in the IDS is proposed. Finally, the current limitations are discussed and future research directions are provided.
DOI: 10.34028/iajit/19/3A/10
Appears in Collections:Fulltext Publications

Files in This Item:
File Description SizeFormat
The Intrusion Detection System by Deep Learning Methods Issues and Challenges.pdf764.69 kBAdobe PDFView/Open
Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.