Detecting and correcting malicious data in VANETs

TLDR

Vehicular ad hoc networks depend on node‑to‑node communication, which permits malicious data traffic, and the easy access to information makes data validation a difficult security goal. The study proposes a general method for assessing the validity of data in VANETs. The method has each node search for explanations of its collected data, score them according to consistency with a VANET model, and accept the data from the highest‑scoring explanation, assuming nodes can distinguish some others and that parsimony reflects adversarial behavior. The authors justify these assumptions and demonstrate the approach on specific VANET scenarios.

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.