MARINE: Man-in-the-Middle Attack Resistant Trust Model in Connected Vehicles

TLDR

Vehicular ad hoc networks enhance traffic efficiency and safety but expose connected vehicles to man‑in‑the‑middle attacks that can corrupt shared data. The study aims to establish a trust framework that ensures only authentic, accurate, and trusted content is propagated among vehicles. MARINE identifies and revokes MiTM attackers by first performing multidimensional plausibility checks to establish sender trust, then evaluating received data directly and indirectly, and is evaluated through extensive simulations against three attacker models. Simulations with 35 % MiTM attackers show MARINE improves precision by 15 %, recall by 18 %, and F‑score by 17 % over the leading trust model.

Abstract

Vehicular ad hoc network (VANET), a novel technology, holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as man-in-the-middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate, and trusted content within the network. In this article, we propose a novel trust model, namely, MiTM attack resistance trust model in connected vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multidimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data are then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the benchmarked trust model. The simulation results show that for a network containing 35% of MiTM attackers, MARINE outperforms the state-of-the-art trust model by 15%, 18%, and 17% improvements in precision, recall, and F-score, respectively.