Consensus Algorithm with Censored Data for Distributed Detection with Corrupted Measurements: A Game-Theoretic Approach

In distributed detection based on consensus algorithm, all nodes reach the same decision by locally exchanging information with their neighbors. Due to the distributed nature of the consensus algorithm, an attacker can induce a wrong decision by corrupting just a few measurements. As a countermeasure, we propose a modified algorithm wherein the nodes discard the corrupted measurements by comparing them to the expected statistics under the two hypothesis. Although the nodes with corrupted measurements are not considered in the protocol, under proper assumptions on network topology, the convergence of the distributed algorithm can be preserved. On his hand, the attacker may try to corrupt the measurements up to a level which is not detectable to avoid that the corrupted measurements are discarded. We describe the interplay between the nodes and the attacker in a game-theoretic setting and use simulations to derive the equilibrium point of the game and evaluate the performance of the proposed scheme.