Machine learning based novel frameworks developments and architectures for secured communication in VANETs for smart transportation

Abstract

Vehicle ad hoc networks have made intelligent transportation systems that significantly increase road safety as well as management possible. Vehicles can now communicate and share information about the road using this new technology. However, malicious users might inject fake emergency alerts into vehicular ad hoc network (VANET), making it impossible for nodes to access accurate road information. In vehicular ad hoc networks, assessing credibility of nodes has become a crucial task to ensure reliability as well as trustworthiness of data. Using machine learning methods, this study proposes a novel security technique that improves communication and intruder detection in VANET for smart transportation. Ciphertext-policy game theory encryption analysis for smart transportation is used here to improve the security of the VANET. Fuzzy rule-based encoder perceptron neural networks are utilized in the detection of the VANET intruder. For a variety of network datasets, the experimental analysis is conducted in terms of throughput, QoS, latency, computational cost, and data transmission rate.