Spectrum Sensing Falsification Detection in Dense Cognitive Radio Networks using a Greedy Method

This paper presents a method for detecting a set of spectrum sensing data falsification (SSDF) attacks, in a geographic database (GDB) enabled cognitive radio (CR) system. Viewing the GDB as a type of non-orthogonal CS dictionary, the composite power spectral density (PSD) estimate at a candidate user is approximated by a small number of sensor nodes listed in the GDB. In a dense CR network, the PSD estimate at a CR may contain a mixture of spectrally overlapping signals. An implementation of the greedy algorithm orthogonal matching pursuit (OMP) is proposed to return a set of sensor nodes which are suspected to be in the vicinity of the CR. A sufficient match between the PSD estimate reported by a candidate user and the PSD that is sparsely approximated from the SNs in its area provides a confidence/trust metric, which can be used to detect potential SSDF attacks. Specific SSDF attacks are reviewed and some recent methods in optimal sensing matrix construction for CS using an overcomplete dictionary are applied to address some of the key operational challenges in this scenario. Simulations provide insight into the detection performance and show that the specified SSDF attacks can be detected amidst additive white Gaussian noise and dictionary mismatches.