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

Abstract

Vehicular ad hoc networks (VANETs) use inter-vehicle communications to improve traffic safety by broadcasting information such as vehicle speed, location and heading to other vehicles. This approach depends on each vehicle advertising its location information. Since the pseudonyms (identifiers) of the vehicles are also broadcast, attackers can track any vehicle of interest, violating privacy of the drivers. The most widely accepted defense is continuous pseudonym updates. We present BOTVEILLANCE, an adaptive cooperative surveillance attack by vehicular botnets—effective even against the best existing pseudonym changing scheme. It is the first long-range global-scale surveillance attack that is solely performed by vehicles themselves without depending on any additional hardware. Since we use our vehicular bots, our surveillance attack is not confined to a specific area. We show via simulation that our attack can keep a vehicle under surveillance up to 85 percent of its route, and identify its destination address 90 percent of the time.