The Study On Anti-SSDF Attack Of Cooperative Spectrum Sensing

Cooperative spectrum sensing,which is an important way for the operation of frequency spectrum sensing in the field of cognitive radio network,is not only able to improve the sensing performance,but also facing a safety problem caused by potential attacking nodes.In the transmission of sensing data to the fusion center,the attacking nodes will lead to sending misleading data to the fusion center,therefore,the performance of the whole cooperative spectrum sensing system will be compromised.The situation when a misjudgment is produced due to the false information transmitted to the fusion center is called the Spectrum Sensing Data Falsification(SSDF)attack.The SSDF attack will not only compromise the sensing performance,but also degrade the use efficiency of the frequency spectrum,which is unallowable in the operation of cognitive radio network.Therefore,it`s significant to explore the algorithm to keep off SSDF attack,which will guarantee the performance of the cooperative spectrum sensing.As the attack node can take a variety of attacks,based on this,this paper presents a variety of attacks against anti-SSDF algorithm.The algorithm mainly includes two steps,the first step,the selection of malicious data: the fusion center first uses the OGK algorithm to estimate the mean and variance of the received perceptual data set,and then selects the malicious data based on the estimated value;The second step,malicious data processing: K-means clustering method to re-estimate the value of malicious data,and with the new estimates instead of malicious data,and then the final fusion decision.The simulation results show that the algorithm proposed in this paper can not only reduce the influence of malicious nodes effectively,but also avoid the historical correlation of nodes and resist the attacks of multiple attacks.At present,most of the literature assumes that the spectrum sensing scene is homogeneous scenario,that is,the sensing data of each node are similar.But the reality of the spectrum sensing scene of life is heterogeneous scenario,in which the sensing data is not the same.Based on this,this paper proposes an algorithm that can be applied not only to heterogeneous scenario but also to the more intelligent cumulative reputation attack.The algorithm mainly includes two aspects: perceived node state partitioning and perceptual data weighted fusion.In terms of perceived node state division,the perceived nodes are divided into three states: discarded,to be determined,and trusted according to the reputation of the perceived nodes.Only nodes that are in a trusted state can participate in the final fusion decision.In the case of perceptual data weighted fusion,the ring-based data is calculated based on the historical value of the perceived data.Then the OGK algorithm is used to estimate the mean and variance of the rings of each node.Finally,the weights are assigned according to different degree of deviation and the fusion is calculated.The simulation results show that the proposed algorithm can guarantee the good perceived performance of the system in homogeneous scenario and heterogeneous scenario,and also effectively resist the cumulative reputation.