Detecting Byzantine attack in cognitive radio networks using machine learning

One primary function in a cognitive radio network (CRN) is spectrum sensing. In an infrastructure-based CRN, instead of individual nodes independently sensing the presence of the incumbent signal and taking decisions thereon, a fusion center (FC) aggregates the sensing reports from the individual nodes and makes the final decision. Such collaborative spectrum sensing (CSS) is known to result in better sensing accuracy. On the other hand, CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack (a.k.a. Byzantine attack) wherein a node maliciously falsifies the sensing report prior to sending it to the FC, with the intention of disrupting the spectrum sensing process. This paper investigates the use of machine learning techniques, viz., SVM, Neural Network, Naive Bayes and Ensemble classifiers for detection of SSDF attacks in a CRN where the sensing reports are binary (i.e., either 1 or 0). The learning techniques are studied under two experimental scenarios: (a) when the training and test data are drawn from the same data-set, and (b) when separate data-sets are used for training and testing. Under the first scenario, of all the techniques, NN and Ensemble are the most robust showing consistently very good performance across varying presence of attackers in the system. Moreover performance comparison with an existing non-machine learning technique shows that the learning techniques are generally more robust than the existing algorithm under high presence of attackers. Under the second scenario, in a limited environment, Ensemble is the most robust method showing good overall performance.