Abstract
In current scenario of automotive industry, Vehicular Networks are providing enhanced support to improve driver security and efficiency. But still, security is an essential requirement in vehicular ad hoc networks since VANET packets contain life critical information. A rogue node can transmit fake messages to their neighbors. In this paper an effort is made to explore an environment with simulated Sybil attack and its effects on network performance. We propose an efficient and scalable data centric approach based on the comparison of average flow rate or mobility information exchanged between the vehicles in the network. Our approach does not require any help from the infrastructure during attacker detection. Simulation results show the effectiveness of the proposed system to locate rogue nodes and inform their peers to avoid communication with them, by checking a Boolean flag. So the proposed system will make VANETs more fault tolerant and robust against transmission of fake messages.
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Rakhi, S., Shobha, K.R. (2019). Performance Analysis of an Efficient Data-Centric Misbehavior Detection Technique for Vehicular Networks. In: Smys, S., Bestak, R., Chen, JZ., Kotuliak, I. (eds) International Conference on Computer Networks and Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 15. Springer, Singapore. https://doi.org/10.1007/978-981-10-8681-6_30
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DOI: https://doi.org/10.1007/978-981-10-8681-6_30
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