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Network Attack Detection Using an Unsupervised Machine Learning Algorithm

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dc.contributor.author Kumar, Avinash
dc.contributor.author Glisson, William
dc.contributor.author Cho, Hyuk
dc.date.accessioned 2020-01-04T08:32:01Z
dc.date.available 2020-01-04T08:32:01Z
dc.date.issued 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/64537
dc.description.abstract With the increase in network connectivity in today's web-enabled environments, there is an escalation in cyber-related crimes. This increase in illicit activity prompts organizations to address network security risk issues by attempting to detect malicious activity. This research investigates the application of a MeanShift algorithm to detect an attack on a network. The algorithm is validated against the KDD 99 dataset and presents an accuracy of 81.2% and detection rate of 79.1%. The contribution of this research is two-fold. First, it provides an initial application of a MeanShift algorithm on a network traffic dataset to detect an attack. Second, it provides the foundation for future research involving the application of MeanShift algorithm in the area of network attack detection.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Cyber Threat Intelligence and Analytics
dc.subject clustering
dc.subject intrusion detection
dc.subject machine learning
dc.subject networks
dc.subject unsupervised algorithm
dc.title Network Attack Detection Using an Unsupervised Machine Learning Algorithm
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.795
Appears in Collections: Machine Learning and Cyber Threat Intelligence and Analytics


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