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Deep Learning Approach for Encryption Techniques in Vehicular Networks

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Abstract

Wireless communication technology is advancing at a breakneck speed these days. The notion of the network of vehicles on net, which is a very enticing internet of things application, includes a linked car as a crucial component. The technology underpinning this programme includes all words used to define the Web of everything, the internet of all things, artificial intelligence, machine learning. Automobiles are connected to the intelligent transportation systems via intercommunications between sensors and intelligent equipment within and off the surroundings. Because the WSN handles the majority of communication, security is a critical issue. Security is a major problem with WSNs although most of the communication takes place over wireless media, increasing the probability of assaults substantially. Systems for intrusion detection, communication safety and prevention measures should be implemented, as a result, research into intrusion detection and prevention methodologies has estimated primacy in the field of study. We can classify user activities under two categories employing intrusion detection and prevention systems: conventional suspicious behaviors and activities. There is a need to analyze deep learning for wireless communications in order to create effective intrusion detection and prevention systems. The objective of this research is to propose Intrusion prevention algorithms and strategies of systems based on deep packet inspection and deep learning. A deep learning model has been described based on a convolutional Neural Network classifier in this paper. Intrusion detection and intrusion prevention are two elements of the proposed scheme. A massive number of labelled-data can be found in the proposed approach to learn and classify helpful characteristics. A convolutional neural network is used in this work to prevent intrusion into wireless sensor networks. A WSN-DS dataset is used, the recommended system's efficacy is evaluated and tested, and dataset experiments are carried out. The research analysis revealed that the developed approach has a 97% accuracy rate and outperforms the existing method tremendously. In today's smart cities, the research proposal will be useful as in encryptions techniques for Intrusion control In Vehicular Networks.

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  1. Instrumentation & Control Department, Dr. B.R Ambekar National Institute of Technology, Jalandhar, India

    Deepak Choudhary & Roop Pahuja

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  1. Deepak Choudhary
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Correspondence to Deepak Choudhary.

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Choudhary, D., Pahuja, R. Deep Learning Approach for Encryption Techniques in Vehicular Networks. Wireless Pers Commun 125, 1–27 (2022). https://doi.org/10.1007/s11277-022-09538-9

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