BTDS: Bayesian‐based trust decision scheme for intelligent connected vehicles in VANETs
Weidong Fang, Wuxiong Zhang, Yang Liu, Weiming Yang, Zhiwei Gao
Abstract
Summary Intelligent connected vehicles (ICVs) are becoming the mainstream of the global automotive industry in the future, and its information security issues gradually emerge. The heterogeneity of Vehicle Ad hoc NETworks (VANETs) and the mobility of terminal nodes make the defense against the security attacks very complex, especially the internal attacks. In this article, toward defense against the internal attacks for ICVs in VANETs, we first propose a Gaussian‐distribution‐based and third‐party‐recommendation trust management model, which is called GTTMM, to solve the issue that the trust value increases too fast under continuous cooperation. And Bayesian‐based trust decision scheme extending from GTTMM is named as BDTS, which incorporates the Bayesian Network, for defending against a variant of On‐Off attack, called high trust value On‐Off attack (HTV‐On‐Off attack). With the simulation study, it is demonstrated that, compared with reputation‐based framework for high integrity sensor networks (RFSN), a traditional trust management model under the Beta distribution, GTTMM can better satisfy the principle of trust value, that is, “hard to get, easy to lose”, while BDTS can defend against the HTV‐On‐Off attack effectively.