Detecting and Preventing Misbehaving Intruders in the Internet of Vehicles

Abstract

The advent of new vehicular advances and accessibility of new network access mediums have evolved service providers with heterogeneous-vehicular collaboration. The performance of heterogeneous-vehicular collaboration depends on the possibility of accurate, up-to-date vehicular information shared by Cooperative- Awareness Messages (CAMs) among neighboring vehicles. Although exchanging wrong mobility coordinates leading to disruption on the Internet of Vehicles (IoVs) applicability. To address these issues, a misbehavior detection approach is proposed which acts as a second wall of defense. Our scheme is divided into three phases context procurement, context sharing, and misbehavior detection. Mathematical modeling has been done to evaluate Sybil attack and false message generation attack detection under misbehavior detection. The proposed scheme attains 99% in detecting false message generation attacks and 98.5% in detecting Sybil attacks. Additionally, false-positive rate, overhead detection, and False-Measures are evaluated which demonstrates the effectiveness of our approach.