Ubiquitous Communications and Network Computing. Second EAI International Conference, Bangalore, India, February 8–10, 2019, Proceedings

Research Article

NB-FTBM Model for Entity Trust Evaluation in Vehicular Ad Hoc Network Security

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  • @INPROCEEDINGS{10.1007/978-3-030-20615-4_13,
        author={S. Sumithra and R. Vadivel},
        title={NB-FTBM Model for Entity Trust Evaluation in Vehicular Ad Hoc Network Security},
        proceedings={Ubiquitous Communications and Network Computing. Second EAI International Conference, Bangalore, India, February 8--10, 2019, Proceedings},
        proceedings_a={UBICNET},
        year={2019},
        month={5},
        keywords={NB-Naive Bayesian E-ID Entity Identification E-RP Entity Reputation Trust boundary Fuzzy inference FTB-Fuzzy Trust Boundary},
        doi={10.1007/978-3-030-20615-4_13}
    }
    
  • S. Sumithra
    R. Vadivel
    Year: 2019
    NB-FTBM Model for Entity Trust Evaluation in Vehicular Ad Hoc Network Security
    UBICNET
    Springer
    DOI: 10.1007/978-3-030-20615-4_13
S. Sumithra1,*, R. Vadivel1,*
  • 1: Bharathiar University
*Contact email: sumiphdit@gmail.com, rvadivelit@buc.edu.in

Abstract

Vehicular Ad hoc network (VANET) is developed for exchanging valuable information among vehicles. Therefore they need to ensure the reliability of the vehicle which is sending data. Trustworthiness could be achieved based on two methods. The first method is creating entity trust and the second one is data trust. This research focuses on evaluating the trustworthiness of the sender entity (vehicle). This paper proposes NB-FTBM: Naive Bayesian Fuzzy Trust Boundary Model to find entity trust. NB-FTBM contains two modules namely Entity Identification (E-ID) and Entity Reputation (E-RP). The proposed model quickly identifies the entity identification score and entity reputation score of an entity. These scores fall under the trust boundary line. Based on this boundary level the entity is allowed to take the necessary decision for the information received. The main advantage of this approach is it takes the benefit of Naive Bayesian classifier along with fuzzy logic. The proposed trust model evaluates the trustworthiness of the metrics accurately.