2021
DOI: 10.1108/ijpcc-09-2021-0239
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An optimized deep learning-based trust mechanism In VANET for selfish node detection

Abstract: Purpose This study aims to develop a trust mechanism in a Vehicular ad hoc Network (VANET) based on an optimized deep learning for selfish node detection. Design/methodology/approach The authors built a deep learning-based optimized trust mechanism that removes malicious content generated by selfish VANET nodes. This deep learning-based optimized trust framework is the combination of the Deep Belief Network-based Red Fox Optimization algorithm. A novel deep learning-based optimized model is developed to iden… Show more

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Cited by 8 publications
(1 citation statement)
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“…To compact the learning state space and guess each communication behavior's Q value, the proposed algorithm includes a complex CNN (convolutional neural network) [20]. [21] proposed a model that uses deep learning to detect selfsh vehicles by their trust values. In this method, a deep belief network (DBN) and Red Fox Optimization (RFO) algorithm is used to evaluate the warning message sender and the integrity of the received message on the receiver side.…”
Section: Dsam (Deep Q-network To Suppress the Attack Motivation Of Se...mentioning
confidence: 99%
“…To compact the learning state space and guess each communication behavior's Q value, the proposed algorithm includes a complex CNN (convolutional neural network) [20]. [21] proposed a model that uses deep learning to detect selfsh vehicles by their trust values. In this method, a deep belief network (DBN) and Red Fox Optimization (RFO) algorithm is used to evaluate the warning message sender and the integrity of the received message on the receiver side.…”
Section: Dsam (Deep Q-network To Suppress the Attack Motivation Of Se...mentioning
confidence: 99%