A Mechanism to Localize, Detect, and Prevent Jamming in Connected and Autonomous Vehicles (CAVs)

One of the challenges in Connected and Autonomous Vehicles (CAVs) and Vehicular Ad Hoc Networks (VANETs) is jamming attacks. These attacks present safety concerns and may render the whole communication network ineffective. Designing an anti-jamming solution for CAVs and VANETs is non-trivial because every jamming scenario presents itself with unique characteristics. In this paper, we extend our previous work in Alam (2021) by proposing a scheme that localizes jamming vehicles, maps the jammed area, and prevents the jamming vehicles from sending and/or receiving beacons in the network. To achieve these objectives, we apply Centroid Localization (CL), Graham Scan Hull Algorithm (GSHA), and bloom filter data structure. The novelties of our work are three-fold. 1) We dynamically change the X, Y positions and radii of the jamming vehicles. 2) We implement multiple jamming attack scenarios with multiple jammers and varying number of vehicles. 3) We prevent the jamming vehicles from sending messages in the network. Simulation results show that on average, our model achieves an accuracy of 97.77%, a precision of 99.14%, a recall of 93.54%, and an F1 score of 96.03% for all the attack scenarios implemented.