An efficient authentication scheme with strong privacy preservation for fog-assisted vehicular ad hoc networks based on blockchain and neuro-fuzzy

摘要

Privacy, security and efficiency are important performance issues in vehicular ad hoc network (VANET), which researchers have tried to address in recent years. This research article proposes a lightweight and privacy-preserving certificateless authentication scheme in fog-assisted VANET using blockchain technology and neuro-fuzzy machine learning technique. A new authentication scheme is designed using certificateless signature based on elliptic curve cryptography (ECC) and hash function operation. The scheme utilizes two blockchains to achieve decentralized and transparent transactions and revocation process. In order to prevent denial-of-service attack, in which a roadside unit (RSU) is flooded with a massive amount of fake authentication requests so as to prevent legitimate nodes from being authenticated, a neuro-fuzzy algorithm is implemented to proactively detect and discard any anomalous requests prior to an authentication process. Moreover, the scheme is demonstrated to be semantically secure in the random oracle model (ROM) based on the intractability of the discrete logarithm problem (DLP). A panoptic analysis shows that the scheme possesses outstanding attributes required for a secure vehicular communication system. The experimental simulation is conducted using simulation of urban mobility (SUMO) and the broadly-accepted network simulator NS-3. The results indicate that the proposed scheme has a high efficiency in terms of transmission delay and message delivery rate. The comparative analysis with the state-of-the-art schemes reveals that the proposed scheme has an improvement of 50%–90.5% in computation cost and 38.46%–69.6% in communication overhead. The simulation result of the neuro-fuzzy gives an accuracy of 91.5% and further analysis shows that it significantly reduces the computation burden on the RSU proportionately with increase in the number of malicious messages.