A Differential Game Based Approach Against Objective Function Attack in Cognitive Networks

We study the problem of defense strategy against Objective function attack (OFA) in Cognitive networks (CNs), where the network can be operated at the optimal state by adapting the parameters of its objective function. An OFA attacker can disrupt the parameter adaptation by interfering measures, which results in some operating parameters of the objective function deviating from their optimal settings. We first model the interactive process between the OFA attacker and defense system using differential game theory, and propose a defense strategy by introducing a new metric, namely, threat factor. Then, we obtain the optimal defense strategy by proving the existence of the saddle point of the proposed model. Moreover, this defense strategy is scale-free such that it can conquer a large number of OFAs in a decentralized way. Finally, we conduct extensive simulations to show that the proposed approach is effective.