On resilient consensus against replay attacks in operator-vehicle networks

We consider an operator-vehicle network where each unmanned vehicle is remotely maneuvered by an operator and its inputs are limited. The objective of the operator-vehicle network is to steer the vehicles to a consensus point within a given constraint set by means of coordination among operators and vehicles. Each operator-vehicle pair is attacked by an adversary who is able to maliciously replay the control commands sent from the operator. To play against adversaries, we come up with a novel replay resilient consensus algorithm based on receding-horizon control, and show that the algorithm can guarantee achieving the constrained consensus objective at a geometric rate. Our proposed algorithm shows an analogous resilience property to denial-of-service attacks.