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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Baron Garcia, Anna"],"institution":null,"degree_name":"Doctor of Philosophy in Electrical Engineering & Computer Science","degree_level":"Dissertation - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-10-01T07:00:00Z","date_published":"2022-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:10Z","subjects":["aviation","cybersecurity","aviation cybersecurity","cybersecurity engineering","machine learning","artificial intelligence","avionics","communications","datalinks","Aviation Safety and Security","Digital Communications and Networking","Other Computer Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/700","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Baron Garcia, Anna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-12-31T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Electrical Engineering & Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["aviation","cybersecurity","aviation cybersecurity","cybersecurity engineering","machine learning","artificial intelligence","avionics","communications","datalinks","Aviation Safety and Security","Digital Communications and Networking","Other Computer Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/700"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["Machine Learning and Artificial Intelligence Methods for Cybersecurity Data within the Aviation Ecosystem"]}]}],"canonical_facts":{"dc:creator":["Baron Garcia, Anna"],"dc:date.available":["2023-12-31T08:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://commons.erau.edu/edt/700"],"dc:subject":["aviation","cybersecurity","aviation cybersecurity","cybersecurity engineering","machine learning","artificial intelligence","avionics","communications","datalinks","Aviation Safety and Security","Digital Communications and Networking","Other Computer Engineering"],"dc:title":["Machine Learning and Artificial Intelligence Methods for Cybersecurity Data within the Aviation Ecosystem"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Dissertation - Open Access"],"thesis:degree_name":["Doctor of Philosophy in Electrical Engineering & Computer Science"]},"updated_at":"2026-07-27T19:25:10Z"}