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Title	Deep Neural Network And Trust Management Approach To Secure Smart Transportation Data In Sustainable Smart Cities
ID_Doc	18000
Authors	Khan S.; Khan S.; Sulaiman A.; Reshan M.S.A.; Alshahrani H.; Shaikh A.
Year	2024
Published	ICT Express, 10, 5
DOI	http://dx.doi.org/10.1016/j.icte.2024.08.006
Abstract	Smart transportation, powered by IoT, transforms mobility with interconnected sensors and devices collecting real-time data on traffic, vehicle locations, and passenger needs. This fosters a safer and more sustainable transportation ecosystem, optimizing traffic flow and enhancing public transit efficiency. However, security and privacy challenges emerge in smart transportation systems. Our proposed solution involves a deep neural network (DNN) model trained on extensive datasets from sustainable cities, incorporating historical information like traffic patterns and sensor readings. This model identifies potential malicious nodes, achieving a 90% accuracy rate in predicting threats such as Denial of Service 88%, Whitewash attacks 80%, and Brute Force attacks 75%. This high precision ensures the security and privacy of passenger vehicle data and routes. © 2024 The Author(s)
Author Keywords	Deep neural network; Machine learning; Privacy; Security; Smart transportation

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