Title | A Trust-based Message Passing Algorithm against Persistent SSDF |
Publication Type | Conference Paper |
Year of Publication | 2020 |
Authors | Liu, Jia, Fu, Hongchuan, Chen, Yunhua, Shi, Zhiping |
Conference Name | 2020 IEEE 20th International Conference on Communication Technology (ICCT) |
Keywords | Cognitive radio, cooperative spectrum sensing, false trust, Libraries, malicious users, Mathematical model, message passing, policy-based governance, pubcrawl, radio transmitters, reliability, Resiliency, Resists, Scalability, Sensors |
Abstract | As a key technology in cognitive radio, cooperative spectrum sensing has been paid more and more attention. In cooperative spectrum sensing, multi-user cooperative spectrum sensing can effectively alleviate the performance degradation caused by multipath effect and shadow fading, and improve the spectrum utilization. However, as there may be malicious users in the cooperative sensing users, sending forged false messages to the fusion center or neighbor nodes to mislead them to make wrong judgments, which will greatly reduce the spectrum utilization. To solve this problem, this paper proposes an intelligent anti spectrum sensing data falsification (SSDF) attack algorithm using trust-based non consensus message passing algorithm. In this scheme, only one perception is needed, and the historical propagation path of each message is taken as the basis to calculate the reputation of each cognitive user. Every time a node receives different messages from the same cognitive user, there must be malicious users in its propagation path. We reward the nodes that appear more times in different paths with reputation value, and punish the nodes that appear less. Finally, the real value of the tampered message is restored according to the calculated reputation value. The MATLAB results show that the proposed scheme has a high recovery rate for messages and can identify malicious users in the network at the same time. |
DOI | 10.1109/ICCT50939.2020.9295897 |
Citation Key | liu_trust-based_2020 |