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Conference proceeding
Machine Learning Systems for Connected and Autonomous Vehicle (CAV) Synthesized Datasets
Proceedings of the Third International Conference on Innovations in Computing Research (ICR’24) , Vol.1058, pp.549-560
Lecture Notes in Networks and Systems
International Conference on Innovations in Computing Research (ICR’24), 3rd (Athens, Greece, 08/12/2024–08/14/2024)
08/2024
Web of Science ID: WOS:001598114000048
Abstract
This paper presents on-going research on connected and autonomous vehicle (CAV) security. The over-arching goal of the research is to secure the vehicle-to-everything (V2X) communication systems. While a previous study focused on synthesizing both normal and malicious Basic Safety Message (BSM) CoreData datasets, this research extends that previous study and explores the feasibility of applying machine learning (ML) techniques to classify the synthetized datasets. The empirical study applies four ML models: Random Forest, Extra Trees, AdaBoost, and Gradient Boosting and provides insights into their respective performances. The paper concludes with practical recommendations for future research extensions.
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Details
- Title
- Machine Learning Systems for Connected and Autonomous Vehicle (CAV) Synthesized Datasets
- Publication Details
- Proceedings of the Third International Conference on Innovations in Computing Research (ICR’24) , Vol.1058, pp.549-560
- Resource Type
- Conference proceeding
- Conference
- International Conference on Innovations in Computing Research (ICR’24), 3rd (Athens, Greece, 08/12/2024–08/14/2024)
- Publisher
- Springer Nature
- Series
- Lecture Notes in Networks and Systems
- Number of pages
- 12
- Grant note
- 1946442 / UWF Argo Cyber Emerging Scholars (ACES) Program - National Science Foundation (NSF) CyberCorps(R) Scholarship for Service (SFS) Program
- Copyright
- © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Identifiers
- WOS:001598114000048; 99381590968706600
- Academic Unit
- Center for Cybersecurity and AI; Division of Academic Affairs
- Language
- English