Scheme Design for Enhancing SSDF Attack Defense with Packet Filtering

In cooperative spectrum sensing networks, the information sent by spectrum sensors (such as mobile phones, tablets, etc.) is often unreliable due to equipment failures, channel shadow fading and noise. Malicious users in collaborative spectrum sensing network also send erroneous perceptions to confuse and interfere with the decisions of honest users. The transmission of unreliable messages between neighbor nodes will inevitably lead to the deviation and error of the perceived results and greatly reduce the efficiency of cooperative spectrum sensing. In order to solve the above problems, this paper combines belief propagation algorithm with reputation model, and proposes a scheme based on secondary users grouping to defense against SSDF attack. The scheme filters out unreliable information in two phases: firstly, in spectrum sensing phase, grouping the secondary users according to belief propagation algorithm can filter out the users which are unreliable due to equipment failure and other factors. The remaining users will be regarded as normally working users for data fusion. Then, in data fusion phase, reputation values will be used as weighting factors in the belief propagation algorithm to calculate the final decision value. The scheme proposed in this paper adopts defensive measures respectively in the spectrum sensing phase and the data fusion phase, which can effectively filter the unreliable information in the network and reduce the impact of the harsh spectrum environment on the secondary users. Simulation results show that the proposed scheme has fewer iterations and faster convergence. And it can effectively reduce the damage caused by SSDF attacks, improve the accuracy of sensing results and enhance the security of cognitive radio networks.