Congestion Attacks to Autonomous Cars Using Vehicular Botnets

Abstract

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.

Security vulnerabilities of connected vehicle streams and their impact on cooperative driving

“…Mevlut et al [8] demonstrate the problems vehicular botnets introduce to the autonomous car setting via a simulation study. In their work, they focus on producing physical congestion on a given road segment and do not discuss the effects of network level congestion through botnets of compromised cars.…”

BOTVEILLANCE: A Vehicular Botnet Surveillance Attack against Pseudonymous Systems in VANETs

“…We achieve this by taking advantage of powerful adversarial entities-vehicular botnets. Since methods for compromising a single vehicle have been demonstrated in [21] and [23], we show in [10] that compromising multiple vehicles and organizing them into botnets is very feasible. Vehicular botnets allow powerful attacks that are impossible with a single compromised vehicle.…”

Adversarial Classification Under Differential Privacy

“…Although privacy is an important concern for systems collecting user data, it is unfortunately not the only risk, as widespread vulnerabilities [2], [3], [4] can be exploited by attackers to inject false data into the system. For example, attackers can inject fake data in crowdsourced vehicular services [5], [6], [7], [8] to cause non-existent congestion alarms and accidents that never happened, triggering the services to automatically reroute traffic because of false information.…”