Zero-knowledge identity authentication for internet of vehicles: Improvement and application

PLoS One. 2020 Sep 28;15(9):e0239043. doi: 10.1371/journal.pone.0239043. eCollection 2020.

Abstract

The popularity of Internet of Vehicles (IoV) has made people's driving environment more comfortable and convenient. However, with the integration of external networks and the vehicle networks, the vulnerabilities of the Controller Area Network (CAN) are exposed, allowing attackers to remotely invade vehicle networks through external devices. Based on the remote attack model for vulnerabilities of the in-vehicle CAN, we designed an efficient and safe identity authentication scheme based on Feige-Fiat-Shamir (FFS) zero-knowledge identification scheme with extremely high soundness. We used the method of zero-one reversal and two-to-one verification to solve the problem that FFS cannot effectively resist guessing attacks. Then, we carried out a theoretical analysis of the scheme's security and evaluated it on the software and hardware platform. Finally, regarding time overhead, under the same parameters, compared with the existing scheme, the scheme can complete the authentication within 6.1ms without having to go through multiple rounds of interaction, which reduces the additional authentication delay and enables all private keys to participate in one round of authentication, thereby eliminating the possibility that a private key may not be involved in the original protocol. Regarding security and soundness, as long as private keys are not cracked, the scheme can resist guessing attacks, which is more secure than the existing scheme.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Automation / methods*
  • Automobile Driving
  • Automobiles
  • China
  • Computer Security / instrumentation*
  • Computer Security / trends*
  • Confidentiality
  • Crime Victims
  • Excipients
  • Humans
  • Information Systems / instrumentation
  • Information Systems / trends
  • Internet
  • Knowledge
  • Research Design
  • Software

Substances

  • Excipients

Grants and funding

This research was supported by the Innovation Plan for Postgraduate Research of Jiangsu Province in 2014 (Grant No. KYLX1057), National Science Foundation of China (Grant No. 61902156), and Natural Science Foundation of Jiangsu Province (Grant No. BK20180860).