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

被引:22
|
作者
Kumar, Satish [1 ]
Gupta, Sunanda [1 ]
Arora, Sakshi [1 ]
机构
[1] Shri Mata Vaishno Devi Univ, Sch Comp Sci & Engn, Jammu 18232, India
关键词
Intrusion detection; Market research; Computer security; Search engines; Feature extraction; Computer hacking; Machine learning; Citation; machine learning; bio-inspired; intrusion detection system; NIDS; datasets; ANOMALY DETECTION; MACHINE; ENSEMBLE; ALGORITHM; DESIGN; MODEL;
D O I
10.1109/ACCESS.2021.3129775
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:157761 / 157779
页数:19
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