Addressing Polymorphic Attack Strategies with Misbehavior Detection for ITS

Cooperative Intelligent Transportation Systems (cITSs) is one of the Internet of Things (IoT) applications whose purpose is to improve road safety and traffic efficiency. Within this system, vehicles can communicate with one another by establishing a Vehicular Ad-Hoc Network (VANET) along the road section. Although such connectivity facilitates the exchange of information related to road safety and traffic efficiency, it puts the vehicles at risk in that an attacker could compromise one or more vehicles and use them to share false information causing congestions and/or life-threatening accidents. Although several studies tried to address this issue, they assume that the network topology and/or attack behavior is stationary, which is not realistic as the cITS is dynamic in nature and the attackers may have the ability and resources to change their behavior continuously. Therefore, these assumptions are not suitable and lead to low detection accuracy and high false alarms. To this end, this paper proposes a misbehavior detection model that can cope with the dynamicity of both cITS topology and attack behavior. The model starts by addressing the issue of missing data that happensat the early stages of the model formation after a topology change. Then, the deep learning approach is used to select the discriminative features used to train. We expect that the proposed model will help to overcome the limitations of related solutions by detecting attacks that change their behavior continuously