Performance Evaluation and Comparison of Machine Learning Models in Anomaly Detection of a SCADA ICS
Date of Award
5-9-2024
Document Type
Thesis
Department
Electrical and Computer Engineering
College Name
College of Engineering
Committee Chair
Tommy Morris
Recommended Citation
Vessels, Hugh Charles, "Performance Evaluation and Comparison of Machine Learning Models in Anomaly Detection of a SCADA ICS" (2024). Honors Capstone Projects and Theses. 915.
https://louis.uah.edu/honors-capstones/915
COinS