A Lightweight Authentication with Dynamic Batch-Based Group Key Management Using LSTM in VANET

Abstract

Due to its complexity and mobility, VANET (vehicle ad hoc network) security has long plagued the development of the IoT industry. It is still a big challenge for users to decide the trustworthiness of an anonymous message or the preservation of personal information. Group signature is widely used in VANET anonymous authentication, but the existing solutions suffer from high computation costs in certificate revocation list (CRL) checking and signature verification process. In our scheme, we develop a lightweight protocol based on hashing functions and group keys, which escapes from the heavy computation cost. Then, we propose a dynamic batch-based group key distribution process, which is based on long short-term memory (LSTM) neural network to predict traffic flow and calculate the weight to determine the right time for key update. In this way, our method will significantly reduce computation delay and communication overhead. The security and performance analyses show that our scheme is more efficient in terms of authentication speed while keeping conditional privacy in VANET.