Trust computation through anomaly monitoring in distributed cognitive radio networks

The open philosophy of cognitive radio networks makes them vulnerable to various types of attacks which compromises the efficiency of these networks. One such attack is the Spectrum Sensing Data Falsification (SSDF) attack where malicious nodes report false spectrum occupancy data to others which when used leads to inference that is far from the true spectrum occupancy. Thus, there is a need to identify the malicious nodes or at least find the trustworthiness of nodes such that the data sent by malicious nodes could be filtered out. This paper proposes a scheme for trust based fusion by monitoring anomalies in advertised spectrum usage reports by secondary nodes. Such monitoring leads to evaluation of trust of a node by its neighbors. The calculated trust is then used to determine if a neighbor node´s advertised data could be used for fusion or not. We provide a heuristic trust threshold for nodes to disregard malicious nodes while fusing the data, which holds good for any probability of attack. To validate our model we conduct extensive simulation experiments. Our results show that majority of the nodes are able to fuse data with greater accuracy for various probabilities or intensities of attack. We also compare our results with blind fusion scheme and observe improvement in accuracy of fusion from individual nodes´ as well as overall network´s perspective. We also report a very counter-intuitive observation: at lower probabilities of attack, a malicious node´s contribution to the overall gain in cooperation is more than the damage done.