Inference in the Presence of Byzantines

this paper, we present further results for Cooperative Malicious Byzantine Attack (CMBA), where Byzantines collaborate to make the decision and use this information for the attack. In order to analyze the network performance, we consider KL-Divergence (KLD) to quantify detection performance and minimum fraction of Byzantines needed to blind the network ( ctblind) as a metric. We show that both KLD and ctblind increase when SR noise is added at the honest sensors. When SR noise is added to the fusion center, we analytically show that there is no gain in terms of ctblind or the network-wide performance measured in terms of the deflection coefficient. We also model a game between the network and the Byzantines and present a necessary condition for a strategy (SR noise) to be a saddle-point equilibrium.