On the Impact of Sybil-based Attacks on Mobile Crowdsensing for Transportation

Mobile Crowd-Sensing (MCS) enables users with personal mobile devices (PMDs) to gain information on their surroundings. Users collect and contribute data on different phenomena using their PMD sensors, and the MCS system processes this data to extract valuable information for end users. Navigation MCS-based applications (N-MCS) are prevalent and important for transportation: users share their location and speed while driving and, in return, find efficient routes to their destinations. However, N-MCS are currently vulnerable to malicious contributors, often termed Sybils: submitting falsified data, seemingly from many devices that are not truly present on target roads, falsely reporting congestion when there is none, thus changing the road status the N-MCS infers. The attack effect is that the N-MCS returns suboptimal routes to users, causing late arrival and, overall, deteriorating road traffic flow. We investigate exactly the impact of Sybil-based attacks on N-MCS: we design an N-MCS system that offers efficient routing on top of the vehicular simulator SUMO, using the InTAS road network as our scenario. We design experiments attacking an individual N-MCS user as well as a larger population of users, selecting the adversary targets based on graph-theoretical arguments. Our experiments show that the resources required for a successful attack depend on the location of the attack (i.e., the surrounding road network and traffic) and the extent of Sybil contributed data for the targeted road(s). We demonstrate that Sybil attacks can alter the route of N-MCS users, increasing average travel time by 20% with Sybils 3% of the N-MCS user population.