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.

翻译：移动群智感知（MCS）使得拥有个人移动设备（PMD）的用户能够获取周围环境的信息。用户利用其PMD传感器收集并贡献关于不同现象的数据，MCS系统处理这些数据以为最终用户提取有价值的信息。基于MCS的导航应用（N-MCS）在交通领域应用广泛且至关重要：用户在驾驶时分享其位置和速度，作为回报，系统为其提供前往目的地的高效路线。然而，N-MCS目前易受恶意贡献者（通常称为Sybil攻击者）的影响：这些攻击者提交伪造数据，看似来自许多实际上并不存在于目标道路上的设备，在并无拥堵时虚假报告拥堵，从而改变N-MCS推断出的道路状态。攻击的后果是N-MCS向用户返回次优路线，导致迟到，并总体上恶化道路交通流。本文精确研究了基于Sybil的攻击对N-MCS的影响：我们设计了一个N-MCS系统，该系统在车辆模拟器SUMO之上提供高效路由，并使用InTAS道路网络作为我们的场景。我们设计了攻击单个N-MCS用户以及更大用户群体的实验，并基于图论论据选择攻击目标。我们的实验表明，成功攻击所需的资源取决于攻击位置（即周围的道路网络和交通状况）以及针对目标道路的Sybil贡献数据的规模。我们证明，Sybil攻击可以改变N-MCS用户的路线，当Sybil攻击者占N-MCS用户群体的3%时，平均出行时间增加20%。