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Enhancing network intrusion detection through robust machine learning models : a comparative analysis
(University of Missouri--Columbia, 2023)
by combining diverse network packet datasets from sources like the CTU Aposemat IoT23 dataset, Mizzou Cyber Range, and IEEE Dataport. A comprehensive and robust framework for network intrusion detection leveraging machine learning models including Random Forest...
Intelligent user interfaces for internet-of-things based web applications
(University of Missouri--Columbia, 2016)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] There is a growing need for Intelligent User Interfaces to visually make sense of the enormous data that is being created within web applications that ...
Next-generation DevOps for network and compute-intensive applications
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 5/1/2024] DevOps has emerged as a critical technology/practice in the era of rapid digital transformation, significantly enabling automation and streamlining the software as well as infrastructure development ...
User experience and robustness in social virtual reality applications
(University of Missouri--Columbia, 2020)
Cloud-based applications that rely on emerging technologies such as social virtual reality are increasingly being deployed at high-scale in e.g., remote-learning, public safety, and healthcare. These applications increasingly ...
Hierarchical cloud-fog platform for data import from network-edge applications
(University of Missouri--Columbia, 2016)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Communication and coordination in a mass casualty disaster scenario is limited and difficult for medical personnel in the absence of necessary cyber ...