An Adaptive Primary User Emulation Attack Detection Mechanism for Cognitive Radio Networks

  • Qi Dong
  • Yu Chen
  • Xiaohua Li
  • Kai Zeng
  • Roger Zimmermann
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 254)


The proliferation of advanced information technologies (IT), especially the wide spread of Internet of Things (IoTs) makes wireless spectrum a precious resource. Cognitive radio network (CRN) has been recognized as the key to achieve efficient utility of communication bands. Because of the great difficulty, high complexity and regulations in dynamic spectrum access (DSA), it is very challenging to protect CRNs from malicious attackers or selfish abusers. Primary user emulation (PUE) attacks is one type of easy-to-launch but hard-to-detect attacks in CRNs that malicious entities mimic PU signals in order to either occupy spectrum resource selfishly or conduct Denial of Service (DoS) attacks. Inspired by the physical features widely used as the fingerprint of variant electronic devices, an adaptive and realistic PUE attack detection technique is proposed in this paper. It leverages the PU transmission features that attackers are not able to mimic. In this work, the transmission power is selected as one of the hard-to-mimic features due to the intrinsic discrepancy between PUs and attackers, while considering constraints in real implementations. Our experimental results verified the effectiveness and correctness of the proposed mechanism.


Cognitive radio networks (CRNs) Primary user emulation (PUE) attacks Detection Hard-to-mimic features 



Q. Dong, Y. Chen and X. Li are supported by the NSF via grant CNS-1443885. K. Zeng is partially supported by the NSF under grant No. CNS-1502584 and CNS-1464487.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Qi Dong
    • 1
  • Yu Chen
    • 1
  • Xiaohua Li
    • 1
  • Kai Zeng
    • 2
  • Roger Zimmermann
    • 3
  1. 1.Department of Electrical and Computer EngineeringBinghamton UniversityBinghamtonUSA
  2. 2.Volgenau School of EngineeringGeorge Mason UniversityFairfaxUSA
  3. 3.School of ComputingNational University of SingaporeSingaporeSingapore

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