Hyperparameter Optimization-Based Trust Quantification for Misbehavior Detection Systems
Vehicular communication via V2X networks significantly improves road safety, but is vulnerable to data manipulation, which can lead to serious incidents. To address this threat, misbehavior detection systems (MBDs) have been developed to detect such misbehavior. In order to enhance the detection of data manipulation, trust assessment in V2X networks has recently gained increasing attention. Trust assessment takes into account the output of various security mechanisms such as MBDs or Intrusion Detection Systems (IDSs) to detect misbehavior. One particular challenge in trust assessment is the appropriate quantification of the output of these security mechanisms into trust opinions. In this paper, we propose a trust quantification methodology that transforms the output of an MBD into a subjective logic opinion. Furthermore, we apply a hyperparameter optimization approach to determine the optimal parameter set for an MBD. Our evaluation using three MBD variants shows that the optimization approach significantly increased the detectionperformance of all MBDs. The MBD variant that used the optimization approach and our proposed trust quantification methodology achieved the best performance, increasing the F1 score by over 13 % compared to other state-of-the-art MBD variants analyzed in this work.