2017
DOI: 10.1155/2017/9050787
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Abstract: In vehicular networks, trustworthiness of exchanged messages is very important since a fake message might incur catastrophic accidents on the road. In this paper, we propose a new scheme to disseminate trustworthy event information while mitigating message modification attack and fake message generation attack. Our scheme attempts to suppress those attacks by exchanging the trust level information of adjacent vehicles and using a two-step procedure. In the first step, each vehicle attempts to determine the tru… Show more

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Cited by 20 publications
(18 citation statements)
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References 44 publications
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“…Shrestha et al, proposed a combined trust model, where M Eval calculates trust in two steps: (1) First it evaluates trust on the node, where a clustering algorithm distinguishes the honest and dishonest nodes, and categorized them into two separate groups [30]. Second, it calculates trust on the received messages based on the modified threshold random walk algorithm.…”
Section: Combined Trust Models (Ct)mentioning
confidence: 99%
“…Shrestha et al, proposed a combined trust model, where M Eval calculates trust in two steps: (1) First it evaluates trust on the node, where a clustering algorithm distinguishes the honest and dishonest nodes, and categorized them into two separate groups [30]. Second, it calculates trust on the received messages based on the modified threshold random walk algorithm.…”
Section: Combined Trust Models (Ct)mentioning
confidence: 99%
“…In the case of most dishonest vehicles, weighted voting may be biased. Another HTM was proposed by Shrestha et al It calculates trust in vehicles in a fully distributed manner [24]. The trust calculation is divided into two steps, namely the evaluation of the trust of the vehicle, and the second step involves the trust calculation of the information.…”
Section: Related Work a Trust Modelmentioning
confidence: 99%
“…Ahmed et al [18] Weighted voting and logistic regression Shretha et al [19] Clustering and random walk Chen et al [20] Attack-resistant trust model based on DST Dhurandher et al [21] Reputation-based trust management [17]. Upon receiving a message from neighbours, E V N computes a fuzzy-based trust score which depends on three sources: (1) recommendation provided by adjacent RSU, (2) recommendation given by neighbouring vehicles, and (3) previous reputation of the sender vehicle.…”
Section: Hybrid Trust Modelsmentioning
confidence: 99%
“…Another HTM is proposed by Shrestha et al which calculates trust on the vehicles in a fully distributed manner [19]. The trust is calculated in two steps, i.e., trust is evaluated for the vehicle while and the second step involves trust calculations for the information.…”
Section: Hybrid Trust Models (Htm)mentioning
confidence: 99%