Moving Target Defense for Cyber–Physical Systems Using IoT-Enabled Data Replication

This article proposes a novel moving target defense (MTD) strategy that leverages the versatility of the Internet of Things (IoT) networks to enhance the security of cyber–physical systems (CPSs) by replicating relevant sensory and control signals. The replicated data are randomly selected and transmitted to create two layers of uncertainties that reduce the ability of adversaries to launch successful cyberattacks, without affecting the performance of the system in a normal operation. The theoretical foundations of designing the IoT network and optimal allocation of replicas per signal are developed for linear-time-invariant systems, and fundamental limits of uncertainties introduced by the framework are calculated. The orchestration of the layers and applications integrated in the proposed framework is demonstrated in experimental implementation on a real-time water system over a WiFi network, adopting a data-centric architecture. The implementation results demonstrate that the proposed framework considerably limits the impact of false-data-injection attacks, while decreasing the ability of adversaries to learn details about the physical system operation.