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Opportunistic usage of spectrum owned by licensed (or primary) users is thecornerstone on which the Cognitive Radio technology is built. Unlicensed (orsecondary) users that thus use the spectrum rely opportunistically on spectrumsensing to determine the presence of primary user signal. In such a context, anattacker may mimic the behavior of a primary user (PU) to deceive the secondaryusers (SUs) into believing that a PU signal is present whereas it is not. Suchan attack is known as the Primary User Emulation Attack (PUEA). A malicioususer may launch a PUEA with the intention of grabbing the vacant bands for itsown transmission. Another reason may be to simply disrupt the functioning ofthe Cognitive Radio Network (CRN). This work investigates the use of one-classclassification for detecting PUEA in an infrastructure-based CRN. We opine thatsensing data collected at the fusion center mainly for Collaborative SpectrumSensing (CSS) can be exploited to characterize a PU signal. The PU signalfeatures thus learned can aid in distinguishing a PU signal from a PU signalemulation. In particular, we investigate the use of one-class classificationtechniques, viz., Isolation Forest, Support Vector Machines (SVM), MinimumCovariance Determinant(MCD) and Local Outlier Factor(LOF) for detection of PUEAattacks. Simulation results support the validity of using one-classclassification for detection of PUEA.

Detection Of Primary User Emulation Attack (PUEA) In Cognitive Radio Networks Using One-Class Classification