Abstract: Spectrum sensing is an important part of cognitive radio communication and is under the serious threats of spectrum sensing data falsification (SSDF) in terms of security,which trends dynamically in the attacking methods nowadays.It is difficult to identify dynamic tampered data using traditional defense algorithms because the traditional ways always assume that the atta-cking strength remains unchanged.Aiming at the dynamic SSDF attack,an energy classifier enabled cooperative spectrum sensing algorithm for anti-SSDF attack is proposed.This algorithm firstly analyzes the characteristics of dynamic SSDF attacks,combines with the distance discriminant method to classify the neighbor users.Then it identifies the malicious neighbor users by comparing the classification results with the local results.Afterward the local user establishes a reputation model under the sliding time window based on the information of historical results,thereby updates the reputation values of neighbor users and will eventually implement weighted cooperative spectrum sensing.The simulation results show that compared with the largest deviation-based distributed cooperative spectrum sensing (LDCSS) algorithm and the reputation-based cooperative spectrum sensing (RBCSS) algorithm,this algorithm provides an increased spectrum detection probability by 15% and 16% respectively when the attacking strength is close to the threshold,which not only significantly increases the cooperative spectrum sensing performance of the cognitive network,but also improves the efficiency of spectrum sharing.

Key words: Data falsification, Distance discrimination, Distributed spectrum sensing, Energy detection algorithm, Trust value