TBDDoSA-MD: Trust-Based DDoS Misbehave Detection Approach in Software-defined Vehicular Network (SDVN)

Reliable vehicles are essential in vehicular networks for effective communication. Since vehicles in the network are dynamic, even a short span of misbehavior by a vehicle can disrupt the whole network which may lead to catastrophic consequences. In this paper, a Trust-Based Distributed DoS Misbehave Detection Approach (TBDDoSA-MD) is proposed to secure the Software-Defined Vehicular Network (SDVN). A malicious vehicle in this network performs DDoS misbehavior by attacking other vehicles in its neighborhood. It uses the jamming technique by sending unnecessary signals in the network, as a result, the network performance degrades. Attacked vehicles in that network will no longer meet the service requests from other vehicles. Therefore, in this paper, we proposed an approach to detect the DDoS misbehavior by using the trust values of the vehicles. Trust values are calculated based on direct trust and recommendations (indirect trust). These trust values help to decide whether a vehicle is legitimate or malicious. We simply discard the messages from malicious vehicles whereas the authenticity of the messages from legitimate vehicles is checked further before taking any action based on those messages. The performance of TBDDoSA-MD is evaluated in the Veins hybrid simulator, which uses OMNeT++ and Simulation of Urban Mobility (SUMO). We compared the performance of TBDDoSA-MD with the recently proposed Trust-Based Framework (TBF) scheme using the following performance parameters such as detection accuracy, packet delivery ratio, detection time, and energy consumption. Simulation results show that the proposed work has a high detection accuracy of more than 90% while keeping the detection time as low as 30 s.