Detecting misbehaviors in VANET with integrated root-cause analysis

Abstract

Misbehavior detection schemes (MDSs) form an integral part of misbehaving node eviction in vehicular ad hoc networks (VANETs). A misbehaving node can send messages corresponding to an event that either has not occurred (possibly out of malicious intent), or incorrect information corresponding to an actual event (for example, faulty sensor reading), or both, causing applications to malfunction. While identifying the presence of misbehavior, it is also imperative to extract the root-cause of the observed misbehavior in order to properly assess the misbehavior’s impact, which in turn determines the action to be taken. This paper uses the Post Crash Notification (PCN) application to illustrate the basic considerations and the key factors affecting the reliability performance of such schemes. The basic cause-tree approach is illustrated and used effectively to jointly achieve misbehavior detection as well as identification of its root-cause. The considerations regarding parameter tuning and impact of mobility on the performance of the MDS are studied. The performance of the proposed MDS is found to be not very sensitive to slight errors in parameter estimation.