Discipline: Computer Sciences and Information Management
Subcategory: Computer Science & Information Systems
Eliakin del Rosario - University of the Virgin Islands, St. Thomas
Co-Author(s): Gabriel Ramos, University of the Virgin Islands, St. Thomas Lyssa Williams, University of the Virgin Islands, St. Croix Eniola Adamson, Tennessee State University, TN James Gadson
As cyber threats grow in uniqueness, network protectors are being outclassed. This, in turn, increases cyber security concerns of business owners and the general public. Some solutions to this problem are to enhance existing network protectors, spread awareness about self-defense in the field of Cyber Security, or develop new and improved cyber tools. To achieve such task, a platform containing a substantial amount of tools is essential. These tools can range from machine learning applications to algorithms simulation. This project presents an open source integrated framework that contains tools necessary to make the development and experimentation of cyber tools possible. Using machine learning, data modeling, and algorithm simulation, we can build models that can monitor and detect anomalies and unique threats in a network from patterns obtained from known data. The logic can then be incorporated into Intrusion Detection System signatures and custom-built detection scripts. This process, in return, achieves our goal of enhancing network protectors’ abilities to defend against cyber threats and developing new and improved tools that can handle these tasks.
Funder Acknowledgement(s): DOE NNSA MSIPP Building CyberSecurity Pipeline Grant
Faculty Advisor: Marc Boumedine,