@article{826865,
      recid = {826865},
      author = {He, Xiaofan, and Dai, Huaiyu,},
      title = {Adversary detection for cognitive radio networks /},
      pages = {1 online resource.},
      abstract = {This SpringerBrief provides a comprehensive study of the  unique security threats to cognitive radio (CR) networks  and a systematic investigation of the state-of-the-art in  the corresponding adversary detection problems. In  addition, detailed discussions of the underlying  fundamental analytical tools and engineering methodologies  of these adversary detection techniques are provided,  considering that many of them are quite general and have  been widely employed in many other related fields. The  exposition of this book starts from a brief introduction of  the CR technology and spectrum sensing in Chapter 1. This  is followed by an overview of the relevant security  vulnerabilities and a detailed discussion of two security  threats unique to CR networks, namely, the primary user  emulation (PUE) attack and the Byzantine attack. To better  prepare the reader for the discussions in later chapters,  preliminaries of analytic tools related to adversary  detection are introduced in Chapter 2. In Chapter 3, a  suite of cutting-edge adversary detection techniques  tailor-designed against the PUE and the Byzantine attacks  are reviewed to provide a clear overview of existing  research in this field. More detailed case studies are  presented in Chapters 4 – 6. Specifically, a physical-layer  based PUE attack detection scheme is presented in Chapter  4, while Chapters 5 and 6 are devoted to the illustration  of two novel detection techniques against the Byzantine  attack. Concluding remarks and outlooks for future research  are provided in Chapter 7. The primary audience for this  SpringerBrief include network engineers interested in  addressing adversary detection issues in cognitive radio  networks, researchers interested in the state-of-the-art on  unique security threats to cognitive radio networks and the  corresponding detection mechanisms. Also, graduate and  undergraduate students interested in obtaining  comprehensive information on adversary detection in  cognitive radio networks and applying the underlying  techniques to address relevant research problems can use  this SpringerBrief as a study guide. .},
      url = {http://library.usi.edu/record/826865},
      doi = {https://doi.org/10.1007/978-3-319-75868-8},
}