An Empirical Analysis of the Effect of Malicious Users in Decentralised Cognitive Radio Networks

摘要

A novel empirical analysis of a decentralised network under a probabilistic Byzantine attack was performed. A decentralised cognitive radio network was simulated using the Neyman-Pearson Belief Propagation (NP-BP) algorithm for cooperative spectrum sensing (CSS). The network was exposed to malicious users (MUs) that attempted to increase the false alarm or missed detection rates in the sensing of the primary user (PU). The NP-BP algorithm was seen to reduce the occurrence of spectrum sensing (SS) errors (in comparison to likelihood detection) when no MUs were present. The performance of the data fusion algorithm was very sensitive to MUs that increased the false alarm rate of PU detection. Conversely, the network performed much better when missed detection attacks were conducted by the MUs. The NP-BP algorithm tends to prefer moderate network connectivity for optimal SS results.