Network Attack Detection using an Unsupervised Machine Learning Algorithm

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dc.contributor.authorKumar, Avinash
dc.contributor.authorGlisson, William Bradley
dc.contributor.authorBenton, Ryan
dc.date.accessioned2021-09-15T20:53:34Z
dc.date.available2021-09-15T20:53:34Z
dc.date.issued2020
dc.descriptionArticle co-authored by William Glisson that was published in the Proceedings of the 53rd Hawaii International Conference on System Sciences in 2020.en_US
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.en_US
dc.identifier.citationKumar, A., Glisson, W. B., Benton, R. (2020). Network Attack Detection using an Unsupervised Machine Learning Algorithm. Proceedings of the 53rd Hawaii International Conference on System Sciences. https://doi.org/10.24251/HICSS.2020.795en_US
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3197
dc.publisherProceedings of the 53rd Hawaii International Conference on System Sciencesen_US
dc.subjectMachine Learning and Cyber Threat Intelligence and Analyticsen_US
dc.subjectclusteringen_US
dc.subjectintrusion detectionen_US
dc.subjectmachine learningen_US
dc.subjectnetworksen_US
dc.subjectunsupervised algorithmen_US
dc.titleNetwork Attack Detection using an Unsupervised Machine Learning Algorithmen_US
dc.typeArticleen_US

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