Kumar, AvinashGlisson, William BradleyBenton, Ryan2021-09-152021-09-152020Kumar, 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.2020.795https://hdl.handle.net/20.500.11875/3197Article co-authored by William Glisson that was published in the Proceedings of the 53rd Hawaii International Conference on System Sciences in 2020.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.Machine Learning and Cyber Threat Intelligence and Analyticsclusteringintrusion detectionmachine learningnetworksunsupervised algorithmArticle