A Game-Theoretic Framework for Optimum Decision Fusion in the Presence of Byzantines
Andrea Abrardo, Mauro Barni, Kassem Kallas, Benedetta Tondi

TL;DR
This paper introduces a game-theoretic framework for decision fusion in networks with malicious Byzantine nodes, enabling optimal performance with limited prior knowledge and analyzing strategic behaviors.
Contribution
It develops a novel game-theoretic approach to decision fusion that relaxes the need for exact Byzantine behavior knowledge, providing equilibrium strategies and performance insights.
Findings
The framework improves data fusion accuracy under Byzantine attacks.
Equilibrium analysis reveals strategies where Byzantines minimize mutual information.
Simulation results demonstrate robustness of the proposed approach.
Abstract
Optimum decision fusion in the presence of malicious nodes - often referred to as Byzantines - is hindered by the necessity of exactly knowing the statistical behavior of Byzantines. By focusing on a simple, yet widely studied, set-up in which a Fusion Center (FC) is asked to make a binary decision about a sequence of system states by relying on the possibly corrupted decisions provided by local nodes, we propose a game-theoretic framework which permits to exploit the superior performance provided by optimum decision fusion, while limiting the amount of a-priori knowledge required. We first derive the optimum decision strategy by assuming that the statistical behavior of the Byzantines is known. Then we relax such an assumption by casting the problem into a game-theoretic framework in which the FC tries to guess the behavior of the Byzantines, which, in turn, must fix their corruption…
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Taxonomy
TopicsDistributed Sensor Networks and Detection Algorithms · Statistical Mechanics and Entropy · Bayesian Modeling and Causal Inference
