A Provenance-Aware Distributed Trust Model for Resilient Unmanned Aerial Vehicle Networks

Abstract

An unmanned aerial vehicle (UAV) network is an emerging industrial IoT network for collaborative UAV communication and management. The open architecture and dynamic topology, which provide functional benefits, unfortunately make UAVNs more vulnerable to a variety of attacks. In UAVNs, malicious nodes not only eavesdrop the communications between UAV nodes but also attempt to attack the entire network by injecting or modifying messages. This work proposes a provenance-aware distributed trust model, named UAV-pro, for UAVNs that aim to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing the message delay and communication cost under resource-constrained network environments. Provenance refers to the history of ownership of messages transmitted on the network. The behavior of message creators and operators can be effectively evaluated based on message integrity, then generate the observational evidence. We collect the observational evidence for distributed trust evaluation, then identify malicious nodes in the network and isolate them from the network. UAVN-pro takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes while ensuring the safe transmission of data by digital signature technology. The experimental results show that UAVN-pro works are compatible with the existing UAV network routing protocols, and can effectively identify attacks, such as the black hole, gray hole, message modification, fake recommendation, and fake identity in UAV networks. UAVN-pro is superior to the existing security model in terms of detection rate, delivery rate, and system energy consumption in most cases.