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Spectrum Sensing in Cognitive Radio Networks Under Security Threats and Hybrid Spectrum Access

  • Kuldeep Yadav
  • Abhijit Bhowmick
  • Sanjay Dhar Roy
  • Sumit Kundu
Chapter
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Spectrum sensing in cognitive radio networks (CRNs) is subjected to some security threats such as primary user emulation (PUE) attack. In PUE attack, malicious users (MUPUE) transmit an emulated primary signal throughout the spectrum sensing interval of secondary users (SUs) to prevent them from accessing the primary user (PU) spectrum band. The performance of spectrum sensing under PUE attack is studied for two types of energy detectors: conventional energy detector (CED) and improved energy detector (IED). In spectrum access, a hybrid spectrum access scheme (a combination of overlay mode and underlay mode) is often promising. A novel analytical expression for SU network throughput under the PUE attack is developed under hybrid spectrum access in this chapter. A threshold optimization technique is studied to reduce the sensing error and improve the network throughput in the presence of attacker. Impact of several parameters such as sensing time, IED parameter, probability of attacker’s presence, attacker strength, maximum allowable SU transmit power, and tolerable interference limit at PU on the SU throughput is investigated. A simulation model is developed based on MATLAB to support our analytical formulations.

References

  1. 1.
    Mitola J, Maguire GQ (1999) Cognitive radio: making software radios more personal. IEEE Pers Commun 6(4):13–18. https://doi.org/10.1109/98.788210 CrossRefGoogle Scholar
  2. 2.
    Haykin S (2005) Cognitive radio: brain-empowered wireless communications. IEEE J Sel Areas Commun 23(2):201–220. https://doi.org/10.1109/JSAC.2004.839380 CrossRefGoogle Scholar
  3. 3.
    Amjad M, Rehmani MH, Mao S (2018) Wireless multimedia cognitive radio networks: a comprehensive survey. IEEE Commun Sur Tutorials:1–49. https://doi.org/10.1109/COMST.2018.2794358
  4. 4.
    Federal Communications Commission (2003) Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies, notice of proposed rulemaking and order, FCC 03–322Google Scholar
  5. 5.
    Arslan H (2007) Cognitive radio, software defined radio, and adaptive wireless systems. Signals and communication technology. Springer, New York. https://doi.org/10.1007/978-1-4020-5542-3 CrossRefGoogle Scholar
  6. 6.
    Urkowitz H (1967) Energy detection of unknown deterministic signals. Proc IEEE 55(4):523–531. https://doi.org/10.1109/PROC.1967.5573 CrossRefGoogle Scholar
  7. 7.
    Chen Y (2010) Improved energy detector for random signals in Gaussian noise. IEEE Trans Wirel Commun 9(2):558–563. https://doi.org/10.1109/TWC.2010.5403535 CrossRefGoogle Scholar
  8. 8.
    Singh A, Bhatnagar MR, Mallik RK (2011) Cooperative spectrum sensing with an Improved energy detector in cognitive radio network. In: Proceedings of NCC 2011, Indian Institute of Science, Bangalore, pp 1–5. https://doi.org/10.1109/NCC.2011.5734777
  9. 9.
    Senthuran S, Anpalagan A, Das O (2012) Throughput analysis of opportunistic access strategies in hybrid underlay overlay cognitive radio networks. IEEE Trans Wirel Commun 11(6):2024–2035. https://doi.org/10.1109/TWC.2012.032712.101209 CrossRefGoogle Scholar
  10. 10.
    Bhowmick A, Prasad B, Roy SD, Kundu S (2016) Performance of cognitive radio network with novel hybrid Spectrum access schemes. Wirel Pers Commun 91(2):541–560. https://doi.org/10.1007/s11277-016-3476-5 CrossRefGoogle Scholar
  11. 11.
    Liang Y-C, Yonghong Z, Peh CY, Hoang AT (2008) Sensing- throughput tradeoff for cognitive radio network. IEEE Trans Wirel Commun 7(4):1326–1337. https://doi.org/10.1109/TWC.2008.060869 CrossRefGoogle Scholar
  12. 12.
    Jianwu L, Zebing F, Zhiyong F, Ping Z (2015) A survey of security issues in cognitive radio networks. China Commun 12(3):132–150. https://doi.org/10.1109/CC.2015.7084371 CrossRefGoogle Scholar
  13. 13.
    Sharma RK, Rawat DB (2015) Advances on security threats and countermeasures for cognitive radio networks: a survey. IEEE Commun Surv Tutorials 17(2):1023–1043. https://doi.org/10.1109/COMST.2014.2380998 CrossRefGoogle Scholar
  14. 14.
    Sharifi AA, Sharifi M, Niya MMJ (2016) Secure cooperative spectrum sensing under primary user emulation attack in cognitive radio networks: attack-aware threshold selection approach. AEU Int J Electron Commun 70(1):95–104. https://doi.org/10.1016/j.aeue.2015.10.010 CrossRefGoogle Scholar
  15. 15.
    Chen R, Park JM (2006) Ensuring trustworthy spectrum sensing in cognitive radio networks. 1st IEEE workshop on networking technologies for software defined radio networks. pp 110–119. https://doi.org/10.1109/SDR.2006.4286333
  16. 16.
    Chen R, Park JM, Reed JH (2008) Defense against primary user emulation attacks in cognitive radio networks. IEEE J Sel Areas Commun 26(1):25–37. https://doi.org/10.1109/JSAC.2008.080104 CrossRefGoogle Scholar
  17. 17.
    Anand S, Jin Z, Subbalakshmi KP (2008) An analytical model for primary user emulation attacks in cognitive radio networks. In: Proceedings of the IEEE 3rd international symposium of new frontiers in dynamic spectrum access networks. pp 1–6. https://doi.org/10.1109/DYSPAN.2008.16
  18. 18.
    Jin Z, Anand S, Subbalakshmi KP (2009) Mitigating primary user emulation attacks in dynamic spectrum access networks using hypothesis testing. ACM SIGMOBILE Mob Comput Commun Rev 13(2):74–85. https://doi.org/10.1145/1621076.1621084 CrossRefGoogle Scholar
  19. 19.
    Shrivastava S, Rajesh A, Bora PK (2015) A simplified counter approach to primary user emulation attacks from secondary user perspective. IEEE 26th annual international symposium on personal, indoor, and mobile radio communications. pp 2149–2154. https://doi.org/10.1109/pimrc.2015.7343653
  20. 20.
    Saber MJ, Sadough SMS (2016) Multiband cooperative spectrum sensing for cognitive radio in the presence of malicious users. IEEE Commun Lett 20(2):404–407. https://doi.org/10.1109/LCOMM.2015.2505299 CrossRefGoogle Scholar
  21. 21.
    Suraweera HA, Smith PJ, Shafi M (2010) Capacity limits and performance analysis of cognitive radio with imperfect channel knowledge. IEEE Trans Veh Technol 59(4):1811–1822. https://doi.org/10.1109/TVT.2010.2043454 CrossRefGoogle Scholar
  22. 22.
    Gradshteyn IS, Ryzhik IM (2007) Table of integrals, series, and products, 7th edn. Academic Press, WalthamzbMATHGoogle Scholar
  23. 23.
    Amjad M, Afzal MK, Umer T, Kim BS (2017) QoS-aware and heterogeneously clustered routing protocol for wireless sensor networks. IEEE Access 5:10250–10262. https://doi.org/10.1109/ACCESS.2017.2712662 CrossRefGoogle Scholar
  24. 24.
    Amjad M, Sharif M, Afzal MK, Kim SW (2016) TinyOS-new trends, comparative views, and supported sensing applications: a review. IEEE Sensors J 16(9):2865–2889. https://doi.org/10.1109/JSEN.2016.2519924 CrossRefGoogle Scholar
  25. 25.
    Khan AA, Rehmani MH, Rachedi A (2017) Cognitive-radio-based internet of things: applications, architectures, Spectrum related functionalities, and future research directions. IEEE Wirel Commun 24(3):17–25. https://doi.org/10.1109/MWC.2017.1600404 CrossRefGoogle Scholar
  26. 26.
    Ren J, Hu J, Zhang D, Guo H, Zhang Y, Shen XJ (2018) RF energy harvesting and transfer in cognitive radio sensor networks: opportunities and challenges. IEEE Commun Mag 56(1):104–110. https://doi.org/10.1109/MCOM.2018.1700519 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Kuldeep Yadav
    • 1
  • Abhijit Bhowmick
    • 2
  • Sanjay Dhar Roy
    • 1
  • Sumit Kundu
    • 1
  1. 1.Department of ECENIT DurgapurDurgapurIndia
  2. 2.Department of CommunicationsSENSE, VIT UniversityVelloreIndia

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