Can Clustering be Used to Detect Intrusion During Spectrum Sensing in Cognitive Radio Networks?

Collaborative sensing helps in achieving a more accurate sensing decision than individual sensing in cognitive radio network (CRN). In an infrastructure-based CRN, each node sends its local sensing report to the fusion center (FC), which uses a fusion rule to aggregate the local sensing reports. However, collaborative sensing is vulnerable to the spectrum sensing data falsification attack, in which a node falsifies its local sensing report before sending it to the FC with the intention of disrupting the final sensing decision of the FC. In practice, the strategy of an attacker is not known. However, the collection of sensing reports at the FC can be useful for data mining with the objective of identifying the attackers. In this paper, we present a method that uses clustering techniques for detection and isolation of such attackers. We employ two clustering techniques, viz., K-medoids clustering and agglomerative hierarchical clustering. Unlike threshold detection that requires some predefined threshold value as input, the proposed approach detects the attackers using only the collection of sensing reports at the FC. We also present how we can use the proposed approach on streaming data (sensing reports), and thus, detect and isolate attackers on the fly. Comparative numerical simulation results support the validity of the approach.