The Intrusion Detection System by Deep Learning

The international arab journal of information technology

article

Methods: Issues and Challenges¹  Antonio García²  Mohammed Jamoos²  Mohammad Alkhanafseh³ 

[1] Department of Computer Science Middle East University;Department of Telematics and Communications University of Granada;Department of Computer Science Birzeit University

关键词: Artificial intelligence;    dataset;    deep learning;    intrusion detection;    machine learning;    security;   

DOI  :  10.34028/iajit/19/3A/10

学科分类：计算机科学（综合）

来源: Zarqa University

【 摘 要 】

Intrusion Detection Systems (IDS) are one of the major research application problems in the computer securitydomain. With the increasing number of advanced network attacks, the improvement of the traditional IDS techniques become achallenge. Efficient ways and methods of identifying, protecting, and analyzing data are needed. In this paper, a comprehensivesurvey on the application of Machine Learning (ML) and Deep Learning (DL) methods on the IDS to increase detection accuracyand reduce error rate is proposed. The recent research papers that have been published between 2018 and 2021 in the area ofapplying ML and DL in the IDS are analyzed and summarized. Four main analyzing aspects are presented as follows: (1) IDSconcepts 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 andweaknesses of each work. Based on the analysis of the literature review papers, a framework for the application of ML and DLin the IDS is proposed. Finally, the current limitations are discussed and future research directions are provided.

【 授权许可】

Unknown