A decision‐making approach for detecting the primary user emulation attack in cognitive radio networks

Abstract

SummaryCognitive radio network (CRN) is a promising technology, which enables secondary users to use the free spectrum channels without causing detrimental interference with the primary user (PU). Nevertheless, CRN is subject to numerous cyber attacks that have a negative impact on its performance. Among the CRN attacks, the primary user emulation (PUE) attack is known to be one of the malicious attacks threatening CRN security. Several attacks detection techniques, based on attacker localization, have been investigated in the literature. These techniques include the trilateration, received signal strength indication (RSSI), and network coding approach as well. However, most of these techniques do not consider the uncertainty related to CRN, which can be modeled by a cost function defined as a weighted sum of conditional probabilities. In this paper, a localization technique, relied on a trilateration computation and a Bayesian model, is proposed for PUE position detection purpose under uncertainty conditions assumption. Particularly, the estimation of PUE position is performed through trilateration method based on RSSI at the anchor nodes for the signal coming from either PU or PUE, whereas, the Bayesian decision model, based on a cost function, is involved to check the PU legitimacy. The simulation results show that the decision‐making approach "Security, productivity, Balancing" influences directly the zone of the PUE attack detection.