CARS: Context Aware Reputation Systems to Evaluate Vehicles' Behaviour

The introduction of new generation ICT systems into vehicles makes them highly connected with the external World. As drawback, vehicle becomes potentially vulnerable to security attacks. Here, we consider a scenario in which Vehicular Networks and a Urban Network work together to realize a defence mechanism based on Reputation Systems. In this way, we are able to identify and isolate possible malicious vehicles acting that could send messages with the aim of reducing the availability of the network. We propose Context Aware Reputation Systems, CARS, able to identify insider attackers and isolate them taking into account contextual conditions derived from sensors spread along the entire urban network. Then, we experimentally evaluate CARS on a real data-set of mobility traces of taxis in Rome to compare the proposed systems with existing ones that do not consider contextual conditions. The preliminary results obtained are promising and show the feasibility and potentiality of CARS.