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Detecting and Preventing Misbehaving Intruders in the Internet of Vehicles

Detecting and Preventing Misbehaving Intruders in the Internet of Vehicles

Richa Sharma, Teek Parval Sharma, Ajay Kumar Sharma
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 21
ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781683182535|DOI: 10.4018/IJCAC.295242
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MLA

Sharma, Richa, et al. "Detecting and Preventing Misbehaving Intruders in the Internet of Vehicles." IJCAC vol.12, no.1 2022: pp.1-21. http://doi.org/10.4018/IJCAC.295242

APA

Sharma, R., Sharma, T. P., & Sharma, A. K. (2022). Detecting and Preventing Misbehaving Intruders in the Internet of Vehicles. International Journal of Cloud Applications and Computing (IJCAC), 12(1), 1-21. http://doi.org/10.4018/IJCAC.295242

Chicago

Sharma, Richa, Teek Parval Sharma, and Ajay Kumar Sharma. "Detecting and Preventing Misbehaving Intruders in the Internet of Vehicles," International Journal of Cloud Applications and Computing (IJCAC) 12, no.1: 1-21. http://doi.org/10.4018/IJCAC.295242

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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.

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