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

作　　者：FENG Guangsheng LIN Junyu ZHAO Qian WANG Huiqiang LYU Hongwu ZHAO Xiaoyu HAN Jizhong LI Bingyang 

机构地区：[1]College of Computer Science and Technology, Harbin Engineering University [2]Institute of Information Engineering, Chinese Academy of Sciences [3]School of Computer and Information Engineering, Harbin University of Commerce

出　　处：《Chinese Journal of Electronics》2018年第4期879-888,共10页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61502118,No.61402127,No.61370212);the Natural Science Foundation of Heilongjiang Province in China(No.F2016028,No.F2016009,No.F2015029)

摘　　要：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.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.

关 键 词：Cognitive networks Objective function Operating parameter Saddle point 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]