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A SURVEY ON THE USE OF DATA CLUSTERING FOR INTRUSION DETECTION SYSTEM IN CYBERSECURITY

Authors
  • Bohara, Binita1
  • Bhuyan, Jay1
  • Wu, Fan1
  • Ding, Junhua2
  • 1 Dept.of Computer Science, Tuskegee University, Tuskegee, AL, USA
  • 2 Dept.of Information Science, University of North Texas, Texas, USA
Type
Published Article
Journal
International journal of network security & its applications
Publication Date
Jan 01, 2020
Volume
12
Issue
1
Pages
1–18
Identifiers
DOI: 10.5121/ijnsa.2020.12101
PMID: 34290487
PMCID: PMC8289996
Source
PubMed Central
Keywords
Disciplines
  • Article
License
Unknown

Abstract

In the present world, it is difficult to realize any computing application working on a standalone computing device without connecting it to the network. A large amount of data is transferred over the network from one device to another. As networking is expanding, security is becoming a major concern. Therefore, it has become important to maintain a high level of security to ensure that a safe and secure connection is established among the devices. An intrusion detection system (IDS) is therefore used to differentiate between the legitimate and illegitimate activities on the system. There are different techniques are used for detecting intrusions in the intrusion detection system. This paper presents the different clustering techniques that have been implemented by different researchers in their relevant articles. This survey was carried out on 30 papers and it presents what different datasets were used by different researchers and what evaluation metrics were used to evaluate the performance of IDS. This paper also highlights the pros and cons of each clustering technique used for IDS, which can be used as a basis for future work.

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