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Network Attack Detection Using an Unsupervised Machine Learning Algorithm

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Title:Network Attack Detection Using an Unsupervised Machine Learning Algorithm
Authors:Kumar, Avinash
Glisson, William
Cho, Hyuk
Keywords:Machine Learning and Cyber Threat Intelligence and Analytics
clustering
intrusion detection
machine learning
networks
show 1 moreunsupervised algorithm
show less
Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/64537
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.795
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Cyber Threat Intelligence and Analytics


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