The increasing popularity and acceptance of VANETs will make the deployment of autonomous vehicles easier and faster since the VANET will reduce dependence on expensive sensors. However, these benefits are counterbalanced by possible security attacks. We demonstrate a VANET-based botnet attack in an autonomous vehicle scenario that can cause serious congestion by targeting hot spot road segments. We show via simulation that the attack can increase the trip times of the cars in the targeted area by orders of magnitude. After 5 minutes, the targeted road becomes completely unusable. More importantly, the effect of such an attack is not confined to a specific hotspot; the congestion can spread to multiple roads and significantly affect the entire urban grid. We show that current countermeasures are not effective, and point to new possible defenses. Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper only), and the author's employer if the paper was prepared within the scope of employment.
The increasing popularity and acceptance of VANETs will make the deployment of autonomous vehicles easier and faster since the VANET will reduce dependence on expensive sensors. Many useful applications will be possible with the usage of VANETs, which will improve the safety and quality of trips for the owners of these vehicles. One of these applications is the avoidance of traffic congestion by smart dynamic rerouting. For scalability, current cloud-based solutions, like Google Maps traffic, update congestion levels after a time interval rather than providing real-time measurements. In this paper, we introduce a vehicle-to-vehicle congestion avoidance mechanism, which detects real-time congestion levels and reroutes vehicles accordingly to minimize their trip times. Our system is highly distributed and is, therefore, not subjected to the limitations of centralized congestion avoidance mechanisms. We show via simulation that our system can significantly decrease the trip times of vehicles as well as the average car density on the map. Our proposed system, with its checkpoint and offline path generation approaches, is more responsive to local congestion level changes and computationally less complex for least congested route calculations than state-ofthe-art congestion avoidance mechanisms.
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