Deep neural network and trust management approach to secure smart transportation data in sustainable smart cities

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.