Zero-Trust-Based Protection Scheme for Users in Internet of Vehicles

Abstract

At present, the Internet of Vehicles technology is developing rapidly, but in the process of networking it may be attacked by hackers. In view of the tampering attack on the user’s device and identity, we establish a multifactor authentication scheme to achieve dual identity authentication through device fingerprint authentication and PKI authentication. In response to the attack by hackers in the application of data transmission, we use the SM series of state secret algorithms for data encryption and take advantage of the immutable and decentralized characteristics of blockchain to ensure the security and integrity of data collection and transmission. Through the zero-trust security network architecture, the security level of the system in the process of data transmission is effectively improved, the identity-centered dynamic access control is carried out, the user’s behavior is monitored in real time, and the malicious nodes are screened and eliminated, which improves the stability and security of the Internet of Vehicles data transmission system. By adopting the identity authentication pass rate, fingerprint authentication pass rate, API authentication pass rate, and SPA packet transmission acceptance rate as the factor set, the evaluation level of the vehicles is calculated. The experimental results show that compared with the traditional boundary-centered security protection, our scheme can protect a wider range of application security, even if there are security problems, the loss is less. Through the training simulation of the convolutional neural network, the classification accuracy of the trust level is improved.