Machine Learning and Artificial Intelligence Methods for Cybersecurity Data within the Aviation Ecosystem

<p>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.</p> <p>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.</p>

Name thesis:degree_name

Doctor of Philosophy in Electrical Engineering & Computer Science

Level thesis:degree_level

Dissertation - Open Access

Discipline thesis:degree_discipline

Electrical Engineering and Computer Science

Year dc:date.available

2022

Author dc:creator

• Baron Garcia, Anna

• aviation
• cybersecurity
• aviation cybersecurity
• cybersecurity engineering
• machine learning
• artificial intelligence
• avionics
• communications
• datalinks
• Aviation Safety and Security
• Digital Communications and Networking
• Other Computer Engineering

Repository record dc:identifier

https://commons.erau.edu/edt/700

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1730