Trust Me, I'm Lying: Enhancing Machine-to-Machine Trust

Abstract

Incorporating smart technology into critical infrastructure (CI) promises substantial efficiency improvements as networks of machines communicate and make rapid decisions autonomously. Yet the promise of greater efficiency that such cyber-physical systems (CPS) bring is tempered by increased fragility unless machine-to-machine (M2M) trust is enhanced, particularly in Internet-of-Things (IoT) networks. This work makes two contributions toward improving M2M trust. First, it proposes a multifaceted trust framework comprised of identity verification, experience, context, and recommendation scores to enable high-integrity M2M interactions. Second, this trust framework is implemented via an IoT-friendly distributed ledger on a physical testbed, where it is shown to identify and mitigate errors due to a compromised system component. This implementation mirrors real-world IoT systems in which resource- constrained endpoint devices pose trust score computation chal-lenges and the number of devices raises scalability obstacles for information sharing among nodes.