Avoiding False Social Conformity in Majority-Malicious Connected Vehicle Networks Employing Consensus-Based Reputation Estimates

Gianna Voce, Henry Griffith, Heena Rathore

Abstract: Connected Vehicles (CVs) utilize real-time data exchange between vehicles and infrastructure to empower cooperative decision-making. This reliance on exchanged data introduces vulnerabilities related to the integrity of the shared data, which may be compromised by either malicious attacks or sensor failures. While consensus-based trust estimation algorithms offer a scalable solution for supporting data integrity, their reliability becomes limited for scenarios in which the majority of vehicle nodes are corrupted, a situation that mimics social conformity norms in humans. The research described herein demonstrates an approach for accurately estimating vehicle trustworthiness under majority-malicious network conditions. The Degroot model for distributed consensus formation is modified for vehicle trust estimation, which serves as input to a state-of-the-art cumulative reputation estimator. By drawing parallels between the behavioral tendencies observed in human social groups and the interactions among CVs, we aim to provide algorithmic enhancements that can mitigate the negative security vulnerabilities of conformity.