A Distributed Strategy for Defensing Objective Function Attack in Large-scale Cognitive Networks

Most of existed strategies for defending OFA (Objective Function Attack)are centralized, only suitable for small-scale networks and stressed on the computation complexity and traffic load are usually neglected. In this paper, we pay more attentions on the OFA problem in large-scale cognitive networks, where the big data generated from the network must be considered and the traditional methods could be of helplessness. In this paper, we first analyze the interactive processes between attacker and defender in detail, and then a defense strategy for OFA based on differential game is proposed, abbreviated as DSDG. Secondly, the game saddle point and optimal defense strategy have proved to be existed simultaneously. Simulation results show that the proposed DSDG has a less influence on network performance and a lower rate of packet loss.More importantly, it can cope with the large range