Telecommunication Systems

, Volume 70, Issue 1, pp 123–140 | Cite as

Defence against PUE attacks in ad hoc cognitive radio networks: a mean field game approach

  • Saim Bin Abdul Khaliq
  • Muhammad Faisal AmjadEmail author
  • Haider Abbas
  • Narmeen Shafqat
  • Hammad Afzal


Cognitive Radio (CR) is an emerging and promising communication technology geared towards improving vacant licensed band utilization, intended for unlicensed users. Security of Cognitive Radio Networks (CRN) is a highly challenging domain. At present, plenty of efforts are in place for defining new paradigms, techniques and technologies to secure radio spectrum. In a distributed cognitive radio ad-hoc network, despite dynamically changing topologies, lack of central administration, bandwidth-constraints and shared wireless connections, the nodes are capable of sensing the spectrum and selecting the appropriate channels for communication. These unique characteristics unlock new paths for attackers. Standard security techniques are not an effective shield against attacks on these networks e.g. Primary User Emulation (PUE) attacks. The paper presents a novel PUE attack detection technique based on energy detection and location verification. Next, a game model and a mean field game approach are introduced for the legitimate nodes of CRN to reach strategic defence decisions in the presence of multiple attackers. Simulation of the proposed technique shows a detection accuracy of \({89\%}\) when the probability of false alarm is 0.09. This makes it 1.32 times more accurate than compared work. Furthermore, the proposed framework for defence is state considerate in making decisions.


Primary user emulation (PUE) attack Game theory Cognitive Radio Network (CRN) 


  1. 1.
    Federal Communications Commission (FCC), FCC online table of frequency allocations, August 31, (2016) [Online]
  2. 2.
    Zhao, Q., & Sadler, B. M. (2007). A survey of dynamic spectrum access. IEEE Signal Processing Magazine, 24(3), 7989.CrossRefGoogle Scholar
  3. 3.
    Lien, S.-Y., Chen, K.-C., & Liang, Y.-C. (2014). Lin Y Cognitive radio resource management for future cellular networks. IEEE Wireless Communication, 21(1), 7079.CrossRefGoogle Scholar
  4. 4.
    Marinho, J., Granjal, J., & Monteiro, E. (2015). A survey on security attacks and countermeasures with primary user detection in cognitive radio networks. EURASIP Journal on Information Security, 114.Google Scholar
  5. 5.
    Spectrum Bridge. White space overview. [Online]:
  6. 6.
    WRAN WG on Broadband Wireless Access Standards, IEEE 802.22 [Online].
  7. 7.
    Jana, S., Zeng, K., & Cheng, W. (2013). Trusted collaborative spectrum sensing for mobile cognitive radio networks. IEEE Transactions on Information Forensics and Security, 8(9), 1497–1507.CrossRefGoogle Scholar
  8. 8.
    Wengui, S., & Yang, L. (2015). A jury-based trust management mechanism in distributed cognitive radio networks. China Communications IEEE, 12(7), 119–126.CrossRefGoogle Scholar
  9. 9.
    Pu, D. (2012). Detecting primary user emulation attack in cognitive radio networks. In Proc. IEEE global telecommunications conf, Dec.Google Scholar
  10. 10.
    Jin, Z., Anand, S., & Subbalakshmi, K. P. (2009). Detecting primary user emulation attacks in dynamic spectrum access networks. In Proc. IEEE Intl Conf. Commun. (ICC). Google Scholar
  11. 11.
    Akhunzada, A., Ahmed, E., Gani, A., Khan, M. K., Imran, M., & Guizani, S. (2015). Securing software defined networks: Taxonomy, requirements, and open issues. IEEE Communications Magazine, 53(4), 3644.CrossRefGoogle Scholar
  12. 12.
    Kumari, S., Khan, M. K., & Atiquzzaman, M. (2015). User authentication schemes for wireless sensor networks: A review. Ad Hoc Networks, 27, 159–194.CrossRefGoogle Scholar
  13. 13.
    Tai, W.-L., Chang, Y.-F., & Chen, Y.-C. (2016). A fast-handover-supported authentication protocol for vehicular ad hoc networks. Journal of Information Hiding and Multimedia Signal Processing, 7(5), 960–969.Google Scholar
  14. 14.
    Ngo, N. M., Unoki, M., Miyauchi, R., & Suzuki, Y. (2014). Data hiding scheme for amplitude modulation radio broadcasting systems. Journal of Information Hiding and Multimedia Signal Processing, 5(3), 324–341.Google Scholar
  15. 15.
    Nadeem, A., & Howarth, M. P. (2013). A survey of MANET intrusion detection and prevention approaches for network layer attacks. IEEE Communications Surveys and Tutorials, 15, 2027–2045.CrossRefGoogle Scholar
  16. 16.
    Yang, H., Luo, H., Ye, F., Lu, S., & Zhang, L. (2004). Security in mobile ad hoc networks: Challenges and solutions. IEEE Transactions on Wireless Communications, 11, 3847.Google Scholar
  17. 17.
    Albers, P., Camp, O. Percher, J. M., Jouga, B., Me, L., & Puttini, R. Security in ad hoc networks: A general intrusion detection architecture enhancing trust based approaches. In Proceedings of the 1st international workshop on wireless information systems.Google Scholar
  18. 18.
    Snort Team. (2014). SNORT User Manual, 2.9.7 ed, Available online at:
  19. 19.
    Bro Team. Bro documentation and manual. Available online at:
  20. 20.
    Marti, S., Giuli, T. J., Lai, K., & Baker, M. (2010). Mitigating routing misbehaviour in mobile ad hoc networks. In Proceedings of the 6th international conference on mobile computing and networking, Boston, MA, pp. 255–265.Google Scholar
  21. 21.
    Zhang, Y., & Lee, W. (2013). Intrusion detection in wireless ad hoc networks. In ACM MOBICOM, pp. 275–283.Google Scholar
  22. 22.
    Albers, P., Camp, O., Percher, J. M., Jouga, B., Me, L., & Puttini, R. (2012). Security in ad hoc networks: A general intrusion detection architecture enhancing trust based approaches. In Proceedings of the 1st international workshop on wireless information systems (WIS-2002) (pp. 1–12).Google Scholar
  23. 23.
    Ferraz, L., et al. (2014). An accurate and precise malicious node exclusion mechanism for ad hoc networks. Ad Hoc Networks, Elsevier, pp. l–14Google Scholar
  24. 24.
    Chen, R., & Park, J.-M. (2006). Ensuring trustworthy spectrum sensing in cognitive radio networks. In First IEEE workshop on networking technologies for software defined radio networks (SDR) (pp. 110–119). VA, September: Reston.Google Scholar
  25. 25.
    Chen, R., Park, J.-M., & Reed, J. H. (2008). Defense against primary user emulation attacks in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 26(1), 25–37.CrossRefGoogle Scholar
  26. 26.
    Huang, L., Xie, L., Yu, H., Wang, W., & Yao, Y. (2010) Anti-PUE attack based on joint position verification in cognitive radio networks. In International conference on communications and mobile computing (CMC), Vol. 2, Shenzhen, China, pp. 169–173.Google Scholar
  27. 27.
    Zhao, C., Wang, W., Huang, L., & Yao, Y. (2009). Anti-PUE attack base on the transmitter fingerprint identification in cognitive radio. In 5th International conference on wireless communications, networking and mobile computing (WiCom 09), Beijing, China, pp. 1–5.Google Scholar
  28. 28.
    Afolabi, O. R., Kim, K., & Ahmad, A. (2009). Secure spectrum sensing in cognitive radio networks using emitters electromagnetic signature. In Proceedings of 18th international conference on computer communications and networks (ICCCN 2009), San Francisco, CA, pp. 1–5.Google Scholar
  29. 29.
    Otrok, H., et al. (2008). A game-theoretic intrusion detection model for mobile ad hoc networks. Elsevier Computer Communications, 31, 708–721.CrossRefGoogle Scholar
  30. 30.
    Liang, X., & Xiao, Y. (2013). Game theory for network security. IEEE Communication. Surveys Tutorials, 15(1), 472486.CrossRefGoogle Scholar
  31. 31.
    Meriaux, F., Varma, V., & Lasaulce, S. Mean field energy games in wireless networks. In Proc. 2012 Asilomar conf. signals, systems., computers.Google Scholar
  32. 32.
    Tembine, H., Vilanova, P., Assaad, M., & Debbah, M. Mean field stochastic games for SINR-based medium access control. In Proc. 2011 intl ICST conf. performance evaluation methodologies tools.Google Scholar
  33. 33.
    Huang, M. Y. Mean field stochastic games with discrete states and mixed players. In Proc. 2012 GameNets.Google Scholar
  34. 34.
    Wang, Y., Yu, F., Tang, H., & Huang, M. (2014). A mean field game theoretic approach for security enhancements in mobile ad hoc networks. IEEE Transactions on Wireless Communications, 13(3), 16161627.Google Scholar
  35. 35.
    Trees, H. L. V. (2001). Detection, estimation, and modulation theory: Part I. New Jersey, USA: Wiley-Inter science.CrossRefGoogle Scholar
  36. 36.
    Tandra, R., & Sahai, A. (2008). SNR walls for signal detection. IEEE Journal of Selected Topics in Signal Processing, 2(1), 417.CrossRefGoogle Scholar
  37. 37.
    Le, T. N., Chin, W.-L., & Lin, Y.-H. Non-cooperative and cooperative PUEA detection using physical layer in mobile OFDM-based cognitive radio networks. In International conference on computing, networking and communications, 24 March 2016.Google Scholar
  38. 38.
    Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE, 55(4), 523–531.CrossRefGoogle Scholar
  39. 39.
    Ayyasamy, A., & Venkatachalapathy, K. (2015). Context aware adaptive fuzzy based QoS routing scheme for streaming services over MANETs. Wireless Networks, 21(2), 421–30.CrossRefGoogle Scholar
  40. 40.
    Ahmadi, M., Shojafar, M., Khademzadeh, A., Badie, K., & Tavoli, R. (2015). A hybrid algorithm for preserving energy and delay routing in mobile ad-hoc networks. Wireless Personal Communications, 85(4), 2485–505.CrossRefGoogle Scholar
  41. 41.
    Cordeschi, N., Amendola, D., & Baccarelli, E. (2015). Distributed and adaptive resource management in cloud-assisted cognitive radio vehicular networks with hard reliability guarantees. Vehicular Communications, 2(1), 1–12.CrossRefGoogle Scholar
  42. 42.
    Zhu, J., Song, Y., Jiang, D., & Song, H. (2016). Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the internet of things. IEEE Access, 4, 4609–4617.CrossRefGoogle Scholar
  43. 43.
    Pei, Y., Liang, Y.-C., Zhang, L., The, K. C., & Li, K. H. (2010). Secure communication over MISO cognitive radio channels. IEEE Transactions on Wireless Communications, 9, 1494502.CrossRefGoogle Scholar
  44. 44.
    Amjad, M. F., Aslam, B. & Zou, C. C. (2013). Reputation aware collaborative spectrum sensing for mobile cognitive radio networks. In MILCOM 2013 (pp. 951–956). San Diego, CA, 18-20.Google Scholar
  45. 45.
    Mneimneh, S., & Bhunia, S. (2017). A game-theoretic and stochastic survivability mechanism against induced attacks in Cognitive Radio Networks. Pervasive and Mobile Computing Archive, 40(C), 577–592.CrossRefGoogle Scholar
  46. 46.
    Hosseini, A., Abolhassani, B., & Hosseini, A. (2017). Secure cognitive radio communication for internet-of-things: Anti-PUE attack based on graph theory. Journal of Computer and Communications, 5, 27–39.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Saim Bin Abdul Khaliq
    • 1
  • Muhammad Faisal Amjad
    • 1
    Email author
  • Haider Abbas
    • 1
  • Narmeen Shafqat
    • 1
  • Hammad Afzal
    • 1
  1. 1.National University of Sciences and TechnologyIslamabadPakistan

Personalised recommendations