Network Attack Detection Using an Unsupervised Machine Learning Algorithm

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dc.contributor.authorKumar, Avinash
dc.contributor.authorGlisson, William
dc.contributor.authorBenton, Ryan
dc.date.accessioned2020-01-04T08:32:01Z
dc.date.available2020-01-04T08:32:01Z
dc.date.issued2020-01-07
dc.description.abstractWith 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.795
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64537
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning and Cyber Threat Intelligence and Analytics
dc.subjectclustering
dc.subjectintrusion detection
dc.subjectmachine learning
dc.subjectnetworks
dc.subjectunsupervised algorithm
dc.titleNetwork Attack Detection Using an Unsupervised Machine Learning Algorithm
dc.typeConference Paper
dc.type.dcmiText

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