3D Research

, 9:11 | Cite as

A Game Theory Based Solution for Security Challenges in CRNs

  • Poonam
  • Chander Kumar Nagpal
3DR Express


Cognitive radio networks (CRNs) are being envisioned to drive the next generation Ad hoc wireless networks due to their ability to provide communications resilience in continuously changing environments through the use of dynamic spectrum access. Conventionally CRNs are dependent upon the information gathered by other secondary users to ensure the accuracy of spectrum sensing making them vulnerable to security attacks leading to the need of security mechanisms like cryptography and trust. However, a typical cryptography based solution is not a viable security solution for CRNs owing to their limited resources. Effectiveness of trust based approaches has always been, in question, due to credibility of secondary trust resources. Game theory with its ability to optimize in an environment of conflicting interests can be quite a suitable tool to manage an ad hoc network in the presence of autonomous selfish/malevolent/malicious and attacker nodes. The literature contains several theoretical proposals for augmenting game theory in the ad hoc networks without explicit/detailed implementation. This paper implements a game theory based solution in MATLAB-2015 to secure the CRN environment and compares the obtained results with the traditional approaches of trust and cryptography. The simulation result indicates that as the time progresses the game theory performs much better with higher throughput, lower jitter and better identification of selfish/malicious nodes.

Graphical Abstract


Ad hoc CRN Cryptography Game theory Security Spectrum sensing Trust 


  1. 1.
    Mitola III, J. (2000). Cognitive radio: An integrated agent architecture for software defined radio. Ph.D. Thesis, Royal Institute of Technology (KTH), Sweden.Google Scholar
  2. 2.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  3. 3.
    Akyildiz, I. F., et al. (2006). NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey. Computer Networks, 50, 2127–2159.CrossRefzbMATHGoogle Scholar
  4. 4.
    Wei, Z., Mallik, R. K. & Khaled, L. (2008). Cooperative spectrum sensing optimization in cognitive radio networks. In IEEE international conference on communications (pp. 3411–3415. Beijing.Google Scholar
  5. 5.
    Yang, G., Wang, J., Luo, J., Wen, O. Y., Li, H., Li, Q., et al. (2016). Cooperative spectrum sensing in heterogeneous cognitive radio networks based on normalized energy detection. IEEE TVT, 65(3), 1452–1463.Google Scholar
  6. 6.
    Liu, Y., Zhong, Z., Wang, G., & Hu, D. (2015). Cyclostationary detection based spectrum sensing for cognitive radio networks. Journal of Communications, 10(1), 74–79.CrossRefGoogle Scholar
  7. 7.
    Clancy, T. C. and Nathan Goergen,” Security in Cognitive Radio Networks: Threats and Mitigation” published in 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008).Google Scholar
  8. 8.
    Attar, H., Tang, A., Vasilikos, A. V., Yu, F. R., & Leung, V. C. M. (2013). Security challenges in cognitive radio networks: Solutions and future research directions. Proceedings of the IEEE, 100(12), 3172–3186.CrossRefGoogle Scholar
  9. 9.
    Tang, H., Wei, Z., Yu, F. R., & Mason, P. (2014). Security enhancements for spectrum sensing and data transmission in cognitive radio mobile ad hoc networks (CR-MANETs). In NATO IST symposium on cognitive radio and future networks, Amsterdam, Netherlands.Google Scholar
  10. 10.
    Wei, Y., Leung, H., Wenqing, C., Siyue, C., & Bokan, C. (2012). Participation in repeated cooperative spectrum sensing: A game theoritic perspective. IEEE Transactions on Wireless Communications, 11(3), 1000–1011.CrossRefGoogle Scholar
  11. 11.
    Wang, Y., Yu, F. R., Tang, H., & Huang, M. (2013). A mean field game theoretic approach for security enhancements in mobile ad hoc networks. In IEEE transaction on wireless communication.Google Scholar
  12. 12.
    Wang, B., Wu, Y., Liu, K. J. R., & Clancy, T. C. (2011). An anti-jamming stochastic game for cognitive radio networks. IEEE Journal on Selected Areas in Communications, 29(4), 877–889.CrossRefGoogle Scholar
  13. 13.
    Saad, W., Han, Z., Debbah, M., Hjorungnes, A., & Basar, T. (2009). Coalitional games for distributed collaborative spectrum sensing in cognitive radio networks. In Proceedings of the IEEE INFOCOM.Google Scholar
  14. 14.
    Srivastava, V., Neel, J., Mackenzie, A. B., Menon, R., Dasilva, L. A., Hicks, J. E., et al. (2005). Using game theory to analyze wireless ad hoc networks. IEEE Communications Surveys and Tutorials, 7(4), 46–56.CrossRefGoogle Scholar
  15. 15.
    Wang, B., Liu, K. J., & Clancy, T. C. (2010). Evolutionary cooperative spectrum sensing game: How to collaborate. IEEE Transactions on Communication, 58(3), 890–900.CrossRefGoogle Scholar
  16. 16.
    Wei, Y., & Liu, K. J. R. (2007). Game theoretic analysis of cooperation stimulation and security in autonomous mobile ad hoc networks. IEEE Transaction on Mobile Computing, 6(5), 507–521.CrossRefGoogle Scholar
  17. 17.
    Yan, C., & Liu, K. J. R. (2011). Indirect reciprocity game modelling for cooperation stimulation in cognitive radio network. IEEE Transaction on Communications, 59(1), 159–168.CrossRefGoogle Scholar
  18. 18.
    Afghah, F., Costa, M., Razi, A., Abedi, A., & Ephremides, A. (2013). A reputation-based stackelberg game approach for spectrum sharing with cognitive cooperation. In IEEE CDC.Google Scholar
  19. 19.
    Mehta, S., Kwak, K. S. (2009). Game theory and cognitive radio based wireless networks. In A. Håkansson, & N. T. Nguyen, R. L. Hartung, R. J. Howlett, L. C. Jain (Eds.), Agent and multi-agent systems: Technologies and applications. KES-AMSTA 2009. Lecture notes in computer SCIENCE (vol. 5559). New York: Springer.Google Scholar
  20. 20.
    Hao, D., Ren, Y., Sakurai, K. (2011). A game theory-Based surveillance mechanism against suspicious insiders in MANETs. In L. Chen, M. Yung (Eds.), Trusted systems. INTRUST 2010. Lecture notes in computer science (vol. 6802). Springer.Google Scholar
  21. 21.
    Rafsanjani, M. K., Aliahmadipour, L., & Javidi, M. M. (2010). An optimal method for detecting internal and external intrusion in MANET. Communications in Computer and Information Science, 120, 71–82.CrossRefGoogle Scholar
  22. 22.
    Li, Z., & Shen, H. (2012). Game-theoretic analysis of cooperation incentive strategies in mobile ad hoc networks. In IEEE Transactions on Mobile Computing, 11, 8.Google Scholar
  23. 23.
    Bennaceur, J., & Idoudi, H., & Saidane, L. (2017). Game-based secure sensing for the CRN.
  24. 24.
    Avinash, K. D., Skeath, S., & David, H. R. (2010). Games of strategy (3rd ed.). New York: W.W. Norton & Company.Google Scholar
  25. 25.
    Paterson, K. G., & Price, G. (2003). A comparison between traditional public key infrastructures and identity-based cryptography. In Information security technical report, 8(3), 57–72. Amsterdam: Elsevier Ltd.Google Scholar
  26. 26.
    Chen, R., Park, J.-M., & Reed, J. (2008). Defense against primary user emulation attacks in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 26, 25–37.CrossRefGoogle Scholar
  27. 27.
    Yu, F. R., & Tang, H. (2010). Distributed node selection for threshold key management with intrusion detection in mobile ad hoc networks. New York: Springer.Google Scholar
  28. 28.
    Liu, Y., Ning, P., & Dai, H. (2010). Authenticating primary users’ signals in cognitive radio networks via integrated cryptographic and wireless link signatures. In 2010 IEEE symposium on security and privacy (pp. 286–301).Google Scholar
  29. 29.
    Liu, F., Wang, X., & Tang, H. (2011). Robust physical layer authentication using inherent properties of channel impulse response. In Proceedings of the IEEE Military communications conference (MILCOM).Google Scholar
  30. 30.
    Refaei, M. T., DaSilva, L. A., Eltoweissy, M., & Nadeem, T. (2010). Adaptation of reputation management systems to dynamic network conditions in ad hoc networks. IEEE Transactions on Computers, 59(5), 707–719.MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zhang, N., Lu, N., Lu, R., Mark, J. W., Xuemin (Sherman) Shen. (2012). Energy-efficient and trust-aware cooperation in cognitive radio networks. In IEEE ICC.Google Scholar
  32. 32.
    Jana, Shraboni, Zeng, Kai, Cheng, Wei, & Mohapatra, Prasant. (2013). Trusted collaborative spectrum sensing for mobile cognitive radio networks. IEEE Transactions on Information Forensics and Security, 8(9), 1497–1507.CrossRefGoogle Scholar
  33. 33.
    Jana, S., Zeng, K., Cheng, W., & Mohapatra, P. (2013). Trusted collaborative spectrum sensing for mobile cognitive radio networks. IEEE Transactions on Information Forensics and Security, 8(9), 1497–1507.CrossRefGoogle Scholar
  34. 34.
    Hyder, Chowdhury S., Grebur, Brendan, Xiao, Li, & Ellison, Max. (2014). Adaptive reputation based clustering against spectrum sensing data falsification attacks. IEEE Transactions on Mobile Computing, 13(8), 1707–1719.CrossRefGoogle Scholar
  35. 35.
    Pei, Q., Ma, L., Li H., Li, Z., Yan, D., & Li, Z. (2015). Reputation-based coalitional games for spectrum allocation in distributed cognitive radio networks. In Communication and information systems security symposium.Google Scholar
  36. 36.
    Naveed, A., & Kanhere, S. S. (2006). Security vulnerabilities in channel assignment of multi-radio multi-channel wireless mesh networks. In Proceedings of IEEE conference in global telecommunications, GLOBECOM (pp. 1–5).Google Scholar
  37. 37.
    Kaligineedi, P., Khabbazian, M., & Bhargava, V. K. (2008). Secure cooperative sensing techniques for cognitive radio systems. In ICC. IEEE international conference on communications (pp. 3406–3410).Google Scholar
  38. 38.
    Akyildiz, I. F., Lee, W. Y., Vuran, M. C., & Mohanty, S. (2008). A survey on spectrum management in cognitive radio networks. In IEEE Communications magazine (pp. 40–48).Google Scholar
  39. 39.
    Zhang, X., & Li, C. (2009b). The security in cognitive radio networks: A survey. In Proceedings of international conference on wireless communications and mobile computing, Leipzig, Germany (pp. 309–313).Google Scholar
  40. 40.
    Jithesh, M., & Harish Kumar, C. (2015). Malicious user detection in cognitive radio networks. International Journal of Science and Research (IJSR), 4(8), 496–501.Google Scholar
  41. 41.
    Parvin, S., Hussain, F. K., Hussain, O. K., Han, S., Tian, B., & Chang, E. (2012). Cognitive radio network security: A survey. Journal of Network and Computer Applications., 35, 1691–1708.CrossRefGoogle Scholar
  42. 42.
    Alrabaee, S., Agarwal, A., Anand, D., & Khasawneh, M. (2012). Game theory for security in cognitive radio networks. In The proceedings of international conference on advances in mobile network, communication and its applications.Google Scholar
  43. 43.
    Bhattacharjee, S., Sengupta, S., & Chatterjee, M. (2013). Vulnerabilities in cognitive radio networks: A survey. Computer Communications, 36, 1387–1398.CrossRefGoogle Scholar
  44. 44.
    Hlavacek, D., & Chang, J. M. (2014). A layered approach to cognitive radio network security: A survey. Computer Networks, 75, 414–436.CrossRefGoogle Scholar
  45. 45.
    Marchang, Ningrinla, & Rajkumari, Roshni. (2016). Modelling and mitigating spectrum sensing non-cooperation attack in cognitive radio network. International Journal of Ad Hoc and Ubiquitous Computing, 1, 1. Scholar
  46. 46.
    Amjad, M., & Faisal, M. (2015). Opportunistic spectrum utilization by cognitive radio networks: Challenges and solutions. Electronic Theses and Dissertations (pp. 49).Google Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringYMCA University of Science and TechnologyFaridabadIndia

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