Intelligent Connected Vehicles Cybersecurity Vulnerability Rating Methodology Based on Multiple Factors

Abstract

With the continuous development of intelligent and networked automobiles, the scale of the automotive software system structure is getting larger and larger, and the possibility of security vulnerabilities is increasing. In order to solve the problems of low adaptability and insufficient accuracy of the results of traditional vulnerability scoring and rating rules on the grading of vulnerabilities of intelligent connected vehicles, this paper proposes a multi-factor-based cybersecurity vulnerability rating method for intelligent connected vehicles based on real automobile vulnerability data, by grading the scenario parameters, threat parameters and impact parameters, and using multiple weight calculation methods to obtain the vulnerability rating, so that it is consistent with the subjective ratings obtained from the expert group analysis.