Detecting and correcting malicious data in VANETs

TLDR: A general approach to evaluating the validity of VANET data, where a node searches for possible explanations for the data it has collected based on the fact that malicious nodes may be present and accepts the data as dictated by the highest scoring explanations.

Abstract: In order to meet performance goals, it is widely agreed that vehicular ad hoc networks (VANETs) must rely heavily on node-to-node communication, thus allowing for malicious data traffic. At the same time, the easy access to information afforded by VANETs potentially enables the difficult security goal of data validation. We propose a general approach to evaluating the validity of VANET data. In our approach a node searches for possible explanations for the data it has collected based on the fact that malicious nodes may be present. Explanations that are consistent with the node's model of the VANET are scored and the node accepts the data as dictated by the highest scoring explanations. Our techniques for generating and scoring explanations rely on two assumptions: 1) nodes can tell "at least some" other nodes apart from one another and 2) a parsimony argument accurately reflects adversarial behavior in a VANET. We justify both assumptions and demonstrate our approach on specific VANETs.