Date of Award
Dissertation - Open Access
Doctor of Philosophy in Electrical Engineering & Computer Science
Electrical Engineering and Computer Science
Radu F. Babiceanu
First Committee Member
Laxima Niure Kandel
Second Committee Member
Eduardo A. Rojas Nastrucci
Third Committee Member
Fourth Committee Member
James W. Gregory
Aviation cybersecurity research has proven to be a complex topic due to the intricate nature of the aviation ecosystem. Over the last two decades, research has been centered on isolated modules of the entire aviation systems, and it has lacked the state-of-the-art tools (e.g. ML/AI methods) that other cybersecurity disciplines have leveraged in their fields. Security research in aviation in the last two decades has mainly focused on: (i) reverse engineering avionics and software certification; (ii) communications due to the rising new technologies of Software Defined Radios (SDRs); (iii) networking cybersecurity concerns such as the inter and intra connections of aircraft within the entire ecosystem.
This dissertation presents an overview of the research in aviation cybersecurity and a ‘Machine Learning and Artificial Intelligence Roadmap’ in which several methods are proposed to allow aviation cybersecurity research to benefit from ML/AI and data science methods: a new threat model to frame the cybersecurity threats and an aviation cybersecurity testbed to perform ML/AI experiments.
Scholarly Commons Citation
Baron Garcia, Anna, "Machine Learning and Artificial Intelligence Methods for Cybersecurity Data within the Aviation Ecosystem" (2022). Doctoral Dissertations and Master's Theses. 700.
Available for download on Sunday, December 31, 2023