  We investigate the impact of Byzantine attacks in distributed detection under
binary hypothesis testing. It is assumed that a fraction of the transmitted
sensor measurements are compromised by the injected data from a Byzantine
attacker, whose purpose is to confuse the decision maker at the fusion center.
From the perspective of a Byzantine attacker, under the injection energy
constraint, an optimization problem is formulated to maximize the asymptotic
missed detection error probability, which is based on the Kullback-Leibler
divergence. The properties of the optimal attack strategy are analyzed by
convex optimization and parametric optimization methods. Based on the derived
theoretic results, a coordinate descent algorithm is proposed to search the
optimal attack solution. Simulation examples are provided to illustrate the
effectiveness of the obtained attack strategy.
