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Hyder, C.S., et al. (2011) Defense against Spectrum Sensing Data Falsi Cation Attacks in Cognitive Radio Networks. IEEE Transactions, 59, 774-786.
https://doi.org/10.1109/TSP.2010.2091277

has been cited by the following article:

  • TITLE: Secure Cognitive Radio Communication for Internet-of-Things: Anti-PUE Attack Based on Graph Theory

    AUTHORS: Azar Hosseini, Bahman Abolhassani, Arezoo Hosseini

    KEYWORDS: IoT, PUE, K-Means, Cognitive Radio

    JOURNAL NAME: Journal of Computer and Communications, Vol.5 No.11, September 13, 2017

    ABSTRACT: Internet of Things (IoT) paradigm with strong impact on future life will be interconnected through Cognitive Radio Networks (CRNs). CRNs with Ubiquitous trait are highly promising to achieve interference-free and on-demand services. CRs are able to sense the spectral environment, to detect unoccupied bands, and to use them for signal transmissions. This opportunity encourages malicious Users to surpass CRs by Primary User Emulation (PUE) attack and use vacant spectrums. This paper proposes an unsupervised algorithm to distinguish CRs from PUs regardless of static and mobile user. Employing K-means and graph theory are coincident in our algorithm to improve detection outcomes. The edge of graph corresponding to the relation between signals is used and the result of comparison the signal properties is exposed to different clusters. The Receiver Operating Characteristic (ROC) and Detection Error Tradeoff (DET) of our proposed algorithm prove our claim.