Abstract
With the recent breakthroughs, autonomous driving vehicles (ADVs) are promising to bring transformative changes to our transportation systems. However, recent hacks have demonstrated numerous vulnerabilities in these emerging systems from software to control. Safety is becoming one of the major barriers for the wider adoption of ADVs. ADVs connect to vehicular ad-hoc networks (VANETs) to communicate with each other. However, malicious nodes can falsify information and threaten the safety of passengers and other vehicles with catastrophic consequences. In this work, we present a novel reputation-based intrusion detection scheme to detect malicious ADVs through dynamic credit and reputation evaluation. To further encourage user’s participation, an incentive mechanism is also built for ADVs in the intrusion detection system. We demonstrate the feasibility and effectiveness of our proposed system through extensive simulation, compared with current representative approaches. Simulation results show that our proposed scheme can acquire better intrusion detection results, reduced false positive ratio, and improved user participation.
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Zhai, W., Su, Z. (2020). Intrusion Detection Scheme for Autonomous Driving Vehicles. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_20
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DOI: https://doi.org/10.1007/978-981-15-9129-7_20
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