2024 Design and Research Conference
Cyber Engineering Senior Projects
Integrated Engineering and Science Building 214.
| 1:00 p.m. |
Contract Compliance Monitor (CCM)Team Members: Benjamin Biscomb, Connor Gwatney, Abram Bender, Cade Ruttley Advisor: Dr. Miguel Gates The Contract Compliance Monitor (CCM) is a multi-application system designed to help maintain compliance with the NIST SP 800-171 Rev. 3, a document describing how organizations handling Controlled Unclassified Information (CUI) for the United States government must act. The CCM is able to handle CUI distribution, web filtering (using a web proxy), monitoring of CUI in a local filesystem, and the maintenance of standardized logins across a network. |
| 1:30 p.m. |
gOT network?Team Members: Carson Barnhill, Jeremy Fountain, Cameron McCarthy, Mason Sanchez, Christina Simino Advisor: Dr. Miguel Gates gOT Network is a network enumeration tool developed with the goal of increasing visibility within operational technology networking environments. Operational technology network environments often have low visibility stemming from a variety of criteria, including the type of devices on the network, obtuse network topologies, and a lack of logging. This lack of visibility can lead to non compliant patch management, configuration oversights, and potential vulnerabilities. The gOT Network seeks to help improve this issue of network visibility by connecting to the network and noninvasively collecting available data. Utilizing simple network management protocol with compatible devices such as routers and switches we can query them for their ARP and MAC tables. Using this gathered information we can help to provide a clearer idea of what all a network has to offer. Packaging the final result for IT staff members, they can choose the best course of action when it comes to hardening their network. |
| 2:00 p.m. |
SLaP-E (Small Language Prompt Engineering)Team Members: Stone Gorman, Jordan Williams, Vito Mumphrey, Nathaniel Mitchell, Thomas Rodgers Advisor: Dr. Miguel Gates Small Language Prompt Engineering (SLaP-E) is an application designed to leverage the efficiency and increase the accuracy of small language models using prompt engineering and inferencing techniques. SLaP-E employs prompting techniques such as chain-of-thought, tree of thoughts, and graph of thought, to increase the accuracy of technical answers, as well as inferencing techniques such as six thinking hats and debate, to give more complete answers to open-ended questions. To efficiently use computing resources, each request sent to SLaP-E opens a pipeline, and the pipeline spins up a container holding a language model as the language model is needed. To show the accuracy and resource utilization of SLaP-E when compared with more traditional large language models, we will run multiple prompts for a comparison. |