Multinomial Trust In Presence Of Uncertainty And Adversaries In Dsa Networks

Dynamic spectrum access (DSA) networks allow opportunistic spectrum access to license exempt secondary nodes. Usually secondary nodes employ a cooperative sensing mechanism to correctly infer spectrum occupancy. However, the possibility of falsification of locally sensed occupancy report, also known as secondary spectrum data falsification (SSDF) can cripple the operation of secondary networks. In this paper, we propose a multivariate Bayesian trust model for secondary nodes in a distributed DSA network. The proposed model accurately incorporates anomalous behavior as well as monitoring uncertainty that might arise from an anomaly detection scheme. We also propose possible extensions to the SSDF attack techniques. Subsequently, we use a machine learning approach to learn the thresholds for classifying nodes as honest or malicious based on their trust values. The threshold based classification is shown to perform well under different path loss environments and with varying degrees of attacks by the malicious nodes. We also show the trust based fusion model can be used by nodes to disregard a node\u27s information while fusing the individual reports. Using the fusion scheme, we report the improvements of cooperative spectrum decisions for various multi-channel SSDF techniques.