Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-042413-134848


Document Typedissertation
Author NamePu, Di
URNetd-042413-134848
TitlePrimary User Emulation Detection in Cognitive Radio Networks
DegreePhD
DepartmentElectrical & Computer Engineering
Advisors
  • Alexander M. Wyglinski, Advisor
  • Kaveh Pahlavan, Committee Member
  • Andrew G. Klein, Committee Member
  • Weichao Wang, Committee Member
  • Keywords
  • primary user emulation
  • cognitive radio network
  • Date of Presentation/Defense2013-04-23
    Availability restricted

    Abstract

    Cognitive radios (CRs) have been proposed as a promising solution for improving spectrum utilization via opportunistic spectrum sharing. In a CR network environment, primary (licensed) users have priority over secondary (unlicensed) users when accessing the wireless channel. Thus, if a malicious secondary user exploits this spectrum access etiquette by mimicking the spectral characteristics of a primary user, it can gain priority access to a wireless channel over other secondary users. This scenario is referred to in the literature as primary user emulation (PUE).

    This dissertation first covers three approaches for detecting primary user emulation attacks in cognitive radio networks, which can be classified in two categories. The first category is based on cyclostationary features, which employs a cyclostationary calculation to represent the modulation features of the user signals. The calculation results are then fed into an artificial neural network for classification. The second category is based on video processing method of action recognition in frequency domain, which includes two approaches. Both of them analyze the FFT sequences of wireless transmissions operating across a cognitive radio network environment, as well as classify their actions in the frequency domain. The first approach employs a covariance descriptor of motion-related features in the frequency domain, which is then fed into an artificial neural network for classification. The second approach is built upon the first approach, but employs a relational database system to record the motion-related feature vectors of primary users on this frequency band. When a certain transmission does not have a match record in the database, a covariance descriptor will be calculated and fed into an artificial neural network for classification.

    This dissertation is completed by a novel PUE detection approach which employs a distributed sensor network, where each sensor node works as an independent PUE detector. The emphasis of this work is how these nodes collaborate to obtain the final detection results for the whole network.

    All these proposed approaches have been validated via computer simulations as well as by experimental hardware implementations using the Universal Software Radio Peripheral (USRP) software-defined radio (SDR) platform.

    Files
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