A secure authentication framework for IoV based on blockchain and ensemble learning

Abstract

A secure authentication framework based on blockchain and ensemble learning is proposed to address the problem that vehicle identity privacy data in Internet of Vehicles (IoV) is vulnerable to theft and tampering. First, a secure and efficient authentication method based on blockchain and Physical Unclonable Function (PUF) is implemented, which ensures the identity privacy of the vehicle when accessing IoV, and improves the problem of high resource overhead of the traditional IoV authentication scheme while guaranteeing security, and the computational overhead is about 2.424 ms at the first level of security framework. Secondly, an intrusion detection method based on Whale Optimization Algorithm (WOA) and Extreme Gradient Boosting (XGBoost) is proposed, and the detection model trained based on this method can effectively detect various attacks against IoV. As a security method at the second level of secure framework, the method outperforms related works in detecting malicious attacks with a detection accuracy of 98.41% for ToN-IoT and 99.99% for BoT-IoT.