A decision‐making approach for detecting the primary user emulation attack in cognitive radio networks. (31st July 2019)

• Record Type: Journal Article
• Authors: Fassi Fihri, Wassim; El Ghazi, Hassan; Abou El Majd, Badr; El Bouanani, Faissal
• Is Part Of: International journal of communication systems. Volume 32:Number 15(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-31
• Subjects: Bayesian decision theory -- cognitive radio -- decision tree -- primary user emulation -- RSSI -- trilateration; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.4026
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: Cognitive 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. Abstract : Bayesian model for PU/PUE location estimation based on RSSI/trilateration for distinguishing the attacker from the legitimate PU in uncertain area The proposed uncertainty method relies on both the decision maker cost matrix parameters (Security, Productivity and balanced) and the Bayesian model for stopping any eventual PUE attack lead the decision maker to the adequate decision, deny any potential malicious activities, and avoid any dysfunction of the CRN.