Currently, there are several ongoing efforts for the definition of new regulation policies, paradigms, and technologies aiming a more efficient usage of the radio spectrum. In this context, Cognitive Radio (CR) emerges as one of the most promising players by enabling the dynamic access to vacant frequency bands on a non-interference basis. However, the intrinsic characteristic of CR opens new ways for attackers, namely in the context of the effective detection of incumbent or primary users, the most fundamental and challenging requirement for the successful operation of CR networks. In this article, we provide a global and integrated vision of the main threats affecting CR environments in the context of the detection of primary users, with a particular focus on spectrum sensing data falsification and primary user emulation attacks. We also address solutions and research challenges still required to address such threats. Our discussion aims at being complete and self-contained, while also targeting readers with no specific background on this important topic of CR environments. It is, as far as our knowledge goes, the first work providing a global and clear vision of security threats and countermeasures in the context of primary user detection in CR.
Keywords
Security in Cognitive Radio, Primary User Detection, Primary User Emulation, Spectrum Sensing Falsification
Related Project
iCIS - Intelligent Computing in the Internet of Services
Journal
EURASIP Journal on Information Security, Vol. 2015, #4, April 2015
DOI
Cited by
Year 2017 : 2 citations
Mneimneh, S., Bhunia, S., Vázquez-Abad, F., & Sengupta, S. (2017). A game-theoretic and stochastic survivability mechanism against induced attacks in Cognitive Radio Networks. Pervasive and Mobile Computing.
Sabiq, P. V., & Saraswady, D. (2017). Primary user Emulation Attack Defense in Filter Bank based Cognitive Radio. Indian Journal of Science and Technology, 10(2).
Year 2016 : 5 citations
M. Chen, Y. Qian, S. Mao, W. Tang, and X. Yang, “Software-defined mobile networks security”, Mobile Networks and Applications, pp. 1-15, 2016.
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M. Ramasamy, “A new feature extraction and attacking node classification framework using PDCFE?HPSMM model in cognitive radio networks”, International Journal of Communication Systems, 2016.
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