Abstract
In today’s modern world, intelligent communication system which is called as vehicular ad hoc networks (VANETs) play important hypothesis for sharing the live messages concerning with the traffic jamming, highway safety, and position-related services to progress the driving comportment of the driver. During this situation, privacy with the security is the major challenge to be identified. To identify this issue, Trusted Authority (TA) will provide dual authentication to maintain the authentication of messages between the TA and the VANET nodes. In this work, TA classifies the vehicles into prime users, secondary users, and illegal users under the roadside units (RSUs). Black Widow Optimization (BWO) technique for optimizing the weights to improve the network performance parameters and deep neural networks for intrusion detection, both the advantages are combined in the method. The results of the proposed scheme (BWO-DNN) are computationally efficient in terms of Key Computation Time(121 ms) and Key Recovery Time (3.82 ms) with the existing approaches.
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Sulthana, S., Reddy, B.N.M. (2022). Deep Neural Networks and Black Widow Optimization for VANETS. In: Suma, V., Baig, Z., Kolandapalayam Shanmugam, S., Lorenz, P. (eds) Inventive Systems and Control. Lecture Notes in Networks and Systems, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-19-1012-8_48
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DOI: https://doi.org/10.1007/978-981-19-1012-8_48
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