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Title: A Trust Management Framework for Connected Autonomous Vehicles Using Interaction Provenance
Connected autonomous vehicles (CAVs) have fostered the development of intelligent transportation systems that support critical safety information sharing with minimum latency and making driving decisions autonomously. However, the CAV environment is vulnerable to different external and internal attacks. Authorized but malicious entities which provide wrong information impose challenges in preventing internal attacks. An essential requirement for thwarting internal attacks is to identify the trustworthiness of the vehicles. This paper exploits interaction provenance to propose a trust management framework for CAVs that considers both in-vehicle and vehicular network security incidents, supports flexible security policies and ensures privacy. The framework contains an interaction provenance recording and trust management protocol that extracts events from interaction provenance and calculates trustworthiness using fuzzy policies based on the events. Simulation results show that the framework is effective and can be integrated with the CAV stack with minimal computation and communication overhead.  more » « less
Award ID(s):
1642078
NSF-PAR ID:
10400173
Author(s) / Creator(s):
;
Date Published:
Journal Name:
ICC 2022 - IEEE International Conference on Communications
Page Range / eLocation ID:
2236 to 2241
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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