A Location Validation Technique to Mitigate GPS Spoofing Attacks in IEEE 802.11p based Fleet Operator's Network of Electric Vehicles

• URL: http://arxiv.org/abs/2410.13031v1
• Date: Wed, 16 Oct 2024 20:42:27 GMT
• Title: A Location Validation Technique to Mitigate GPS Spoofing Attacks in IEEE 802.11p based Fleet Operator's Network of Electric Vehicles
• Authors: Ankita Samaddar, Arvind Easwaran,
• Abstract summary: Vehicle rebalancing application uses the GPS location data of the vehicles periodically to determine the vehicle(s) to be moved to a different charging station for rebalancing. A malicious attacker residing in the network can spoof the GPS location data packets of the target vehicle(s) resulting in misinterpretation of the location of the vehicle(s) We propose a location tracking technique that can validate the current location of a vehicle based on its previous location and roadmaps.
• Score: 2.5582913676558205
• License:
• Abstract: Most vehicular applications in electric vehicles use IEEE 802.11p protocol for vehicular communications. Vehicle rebalancing application is one such application that has been used by many car rental service providers to overcome the disparity between vehicle demand and vehicle supply at different charging stations. Vehicle rebalancing application uses the GPS location data of the vehicles periodically to determine the vehicle(s) to be moved to a different charging station for rebalancing. However, a malicious attacker residing in the network can spoof the GPS location data packets of the target vehicle(s) resulting in misinterpretation of the location of the vehicle(s). This can result in wrong rebalancing decision leading to unmet demands of the customers and under utilization of the system. To detect and prevent this attack, we propose a location tracking technique that can validate the current location of a vehicle based on its previous location and roadmaps. We used OpenStreetMap and SUMO simulator to generate the roadmap data from the roadmaps of Singapore. Extensive experiments on the generated datasets show the efficacy of our proposed technique.

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