UCLA

Improving traffic safety is considered as one of the most important tasks by many countries, and current solutions are not very effective due to the human factor. There are 30,000 deaths and 2.2 million injuries caused by car accidents each year just in the US alone. Many of these accidents are due to the lack of sufficient traffic information and slow driver reaction. Vehicular ad hoc networks (VANETs) is a peer-to-peer communication protocol designed to improve traffic safety. In order to achieve this, cars communicate with each other over a wireless channel exchanging information such as their current speeds, locations, heading angles, brake system statuses, etc. VANETs help collect traffic information more accurately and from a much wider area than drivers.

Autonomous driving eliminates the human factor, which will prevent traffic accidents more effectively. Autonomous vehicles are expected to be on the market in 2020. VANET will be used by these cars since it decreases the dependency on expensive sensors, thus making the deployment of autonomous vehicles easier and faster. The accurate traffic information collected using VANETs from a wide area will provide highly effective collision avoidance tools in these autonomous vehicles. However, these benefits are counterbalanced by possible security attacks. Researchers showed that computers inside these vehicles can be compromised; as a result, attackers can take full control of them. Similar to Internet botnets, we can expect that attackers will organize these vehicles into vehicular botnets. We demonstrate the particular dangers of this threat by designing vehicular botnets that consist of these compromised autonomous cars. Existing research into security problems with autonomous vehicles cannot properly cope with our vehicular botnets since they fail to consider the possibility of cooperative malicious cars.

This dissertation investigates the potential uses of vehicular botnets, how they can be organized and controlled, and how we might detect and defend against them. We discuss how an attacker who controls a number of vehicles can organize them into a botnet using the current VANET infrastructure. We demonstrate three powerful attacks performed by our vehicular botnets. We then present our detection mechanism to find vehicular botnets, discuss possible approaches to evict them from the network, and the effect of their eviction. Our work is the first that ever proposed and designed vehicular botnets in the literature. The evaluation of our work is solely through simulations since experimenting with real autonomous vehicles is impractical and dangerous.