Research Trends in Network-Based Intrusion Detection Systems: A Review

Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security...

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Autores principales:	Satish Kumar, Sunanda Gupta, Sakshi Arora
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Citation machine learning bio-inspired intrusion detection system NIDS datasets Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/24eb410d6afb4162ad40d7d97c8c2747
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spelling	oai:doaj.org-article:24eb410d6afb4162ad40d7d97c8c27472021-12-03T00:00:44ZResearch Trends in Network-Based Intrusion Detection Systems: A Review2169-353610.1109/ACCESS.2021.3129775https://doaj.org/article/24eb410d6afb4162ad40d7d97c8c27472021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9623451/https://doaj.org/toc/2169-3536Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security system to detect unauthorized and abnormal network traffic flow. This article presents a review of the research trends in network-based intrusion detection systems (NIDS), their approaches, and the most common datasets used to evaluate IDS Models. The analysis presented in this paper is based on the number of citations acquired by an article published, the total count of articles published related to intrusion detection in a year, and most cited research articles related to the intrusion detection system in journals and conferences separately. Based on the published articles in the intrusion detection field for the last 15 years, this article also discusses the state-of-the-arts of NIDS, commonly used NIDS, citation-based analysis of benchmark datasets, and NIDS techniques used for intrusion detection. A citation and publication-based comparative analysis to quantify the popularity of various approaches are also presented in this paper. The study in this article may be helpful to the novices and researchers interested in evaluating research trends in NIDS and their related applications.Satish KumarSunanda GuptaSakshi AroraIEEEarticleCitationmachine learningbio-inspiredintrusion detection systemNIDSdatasetsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157761-157779 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Citation machine learning bio-inspired intrusion detection system NIDS datasets Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Citation machine learning bio-inspired intrusion detection system NIDS datasets Electrical engineering. Electronics. Nuclear engineering TK1-9971 Satish Kumar Sunanda Gupta Sakshi Arora Research Trends in Network-Based Intrusion Detection Systems: A Review
description	Network threats and hazards are evolving at a high-speed rate in recent years. Many mechanisms (such as firewalls, anti-virus, anti-malware, and spam filters) are being used as security tools to protect networks. An intrusion detection system (IDS) is also an effective and powerful network security system to detect unauthorized and abnormal network traffic flow. This article presents a review of the research trends in network-based intrusion detection systems (NIDS), their approaches, and the most common datasets used to evaluate IDS Models. The analysis presented in this paper is based on the number of citations acquired by an article published, the total count of articles published related to intrusion detection in a year, and most cited research articles related to the intrusion detection system in journals and conferences separately. Based on the published articles in the intrusion detection field for the last 15 years, this article also discusses the state-of-the-arts of NIDS, commonly used NIDS, citation-based analysis of benchmark datasets, and NIDS techniques used for intrusion detection. A citation and publication-based comparative analysis to quantify the popularity of various approaches are also presented in this paper. The study in this article may be helpful to the novices and researchers interested in evaluating research trends in NIDS and their related applications.
format	article
author	Satish Kumar Sunanda Gupta Sakshi Arora
author_facet	Satish Kumar Sunanda Gupta Sakshi Arora
author_sort	Satish Kumar
title	Research Trends in Network-Based Intrusion Detection Systems: A Review
title_short	Research Trends in Network-Based Intrusion Detection Systems: A Review
title_full	Research Trends in Network-Based Intrusion Detection Systems: A Review
title_fullStr	Research Trends in Network-Based Intrusion Detection Systems: A Review
title_full_unstemmed	Research Trends in Network-Based Intrusion Detection Systems: A Review
title_sort	research trends in network-based intrusion detection systems: a review
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/24eb410d6afb4162ad40d7d97c8c2747
work_keys_str_mv	AT satishkumar researchtrendsinnetworkbasedintrusiondetectionsystemsareview AT sunandagupta researchtrendsinnetworkbasedintrusiondetectionsystemsareview AT sakshiarora researchtrendsinnetworkbasedintrusiondetectionsystemsareview
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Research Trends in Network-Based Intrusion Detection Systems: A Review

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