Skip to main content
Log in

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Devroye N, Vu M, Tarokh V (2008) Cognitive radio networks. IEEE Signal Process Mag 25(6):12–23

    Article  Google Scholar 

  2. Carrillo D (2017) Cognitive radio networks. In: Cognitive technologies. Springer, Cham, pp 95–109

    Chapter  Google Scholar 

  3. Ali A, Hamouda W (2017) Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Commun Surv Tutor 19(2):1277–1304

    Article  Google Scholar 

  4. Beibei KJ, Wang Liu (2011) Advances in cognitive radio networks: a survey. IEEE J Sel Top Signal Process 5(1):5–23

    Article  Google Scholar 

  5. Wang J, Huang Y (2010) A cross-layer design of channel assignment and routing in cognitive radio networks. In: IEEE international conference on computer science and information technology (ICCSIT), vol 7, pp 542–547

  6. Pandit S, Singh G (2017) Spectrum sensing in cognitive radio networks: potential challenges and future perspective. In: Spectrum sharing in cognitive radio networks. Springer, Cham, pp 35–75

    Chapter  Google Scholar 

  7. Akyildiz IF, Lee W-Y, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw 50(13):2127–2159

    Article  Google Scholar 

  8. Steenkiste P, Sicker D, Minden G, Raychaudhuri D (2009) Future directions in cognitive radio network research. NSF Workshop Rep 4(1):1–2

    Google Scholar 

  9. Steenkiste P, Sicker D, Minden G, Raychaudhuri D (2009) Future directions in cognitive radio network research. NSF Workshop Rep 4(1):1–2

    Google Scholar 

  10. Ayzed Mirza M, Asif Habib M, Muhammad (2017) Optimized energy ingestion in IoT enabled sensor nodes: a survey. J Softw Eng Intell Syst 2(3):3

    Google Scholar 

  11. Basumatary N, Sarma N, Nath B (2018) Applying classification methods for spectrum sensing in cognitive radio networks: an empirical study. In: Advances in electronics, communication and computing. Springer, Singapore, pp 85–92

    Google Scholar 

  12. Akyildiz IF, Lo BF, Balakrishnan R (2011) Cooperative spectrum sensing in cognitive radio networks: a survey. Phys Commun Sci 4(1):40–62

    Article  Google Scholar 

  13. Karthikeyan CS, Suganthi M (2017) Optimized spectrum sensing algorithm for cognitive radio. Wirel Pers Commun 94(4):2533–2547

    Article  Google Scholar 

  14. Chen Q, Wan P, Wang Y, Li J, Xiao Y (2017) Research on cognitive radio spectrum sensing method based on information geometry. In: International conference on cloud computing and security. Springer, Cham, pp 554–564

    Chapter  Google Scholar 

  15. Shahrasbi B, Rahnavard N, Vosoughi A (2017) Cluster-CMSS: a cluster-based coordinated spectrum sensing in geographically dispersed mobile cognitive radio networks. IEEE Trans Veh Technol 66(7):6378–6387

    Article  Google Scholar 

  16. Awin FA, Abdel-Raheem E, Ahmadi M (2014) Agile hierarchical cluster structure-based cooperative spectrum sensing in cognitive radio networks. In: International conference on microelectronics (ICM), pp 48–51

  17. Hu Z, Bai Y, Cao L, Huang M, Xie M (2018) A sequential compressed spectrum sensing algorithm against SSDH attack in cognitive radio networks. J Electr Comput Eng 2018:1–9. https://doi.org/10.1155/2018/4782718

    Article  MathSciNet  Google Scholar 

  18. Tang H, Vasilakos AV, Yu FR, Leung VCM, Attar A (2012) A survey of security challenges in cognitive radio networks: solutions and future research directions. Proc IEEE 100(12):3172–3186

    Article  Google Scholar 

  19. Zheng M, Liang W, Yu H, Song M (2016) SMCSS: a quick and reliable cooperative spectrum sensing scheme for cognitive industrial wireless networks. IEEE Access 4:9308–9319

    Article  Google Scholar 

  20. Grissa M, Yavuz AA, Hamdaoui B (2017) Preserving the location privacy of secondary users in cooperative spectrum sensing. IEEE Trans Inf Forensics Secur 12(2):418–431

    Article  Google Scholar 

  21. Niranjane PK, Wadhai VM, Rajput SH, Helonde JB (2015) Performance analysis of PUE attacker on dynamic spectrum access in cognitive radio. In: International conference on pervasive computing (ICPC). IEEE, pp 1–6

  22. Anand S, Jin Z, Subbalakshmi KP (2008) An analytical model for primary user emulation attacks in cognitive radio networks. In: 3rd IEEE symposium on new frontiers in dynamic spectrum access networks, DySPAN 2008, pp 1–6

  23. Chen C-Y, Chou Y-H, Chao H-C, Lo C-H (2012) Secure centralized spectrum sensing for cognitive radio networks. Wirel Netw 18(6):667–677

    Article  Google Scholar 

  24. Jiao Y, Yin P, Joe I (2016) Clustering scheme for cooperative spectrum sensing in cognitive radio networks. IET Commun 10(13):1590–1595

    Article  Google Scholar 

  25. Cichon K, Kliks A, Bogucka H (2016) Energy-efficient cooperative spectrum sensing: a survey. IEEE Commun Surv Tutor 18(3):1861–1886

    Article  Google Scholar 

  26. Tang W, Li S, Yu H (2011) Optimization of cooperative spectrum sensing with sensing user selection in cognitive radio networks. EURASIP J Wirel Commun Netw 2:208

    Google Scholar 

  27. Zou Q, Zheng S, Sayed AH (2010) Cooperative sensing via sequential detection. IEEE Trans Signal Process 58:6266–6283

    Article  MathSciNet  Google Scholar 

  28. Noh G, Wang H, Jo J, Kim BH, Hong D (2011) Reporting order control for fast primary detection in cooperative spectrum sensing. IEEE Trans Veh Technol 60(8):4058–4063

    Article  Google Scholar 

  29. Mishra V, Mathew J, Lau C-T (2017) Introduction: QoS and energy management in cognitive radio network. Springer, Cham, pp 1–37

    Book  Google Scholar 

  30. Akyildiz IF, Lee W-Y, Vuran MC, Mohanty S (2006) NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey. Comput Netw Sci 50:2127–2159

    Article  Google Scholar 

  31. Miah MS, Yu H, Godder TK, Rahman MM (2015) A cluster-based cooperative spectrum sensing in a cognitive radio network using eigenvalue detection technique with superposition approach. Int J Distrib Sensor Netw 92

  32. Kishore R, Ramesha CK, Sawant T (2016) Superior selective reporting mechanism for cooperative spectrum sensing in cognitive radio networks. In: International conference wireless communications, signal processing and networking (WiSPNET), pp 426–431

  33. Leon O, Subbalakshmi KP (2017) Cognitive radio network security. In: Handbook of cognitive radio, pp 1–30

    Google Scholar 

  34. Mapunya S, Velempini M (2018) Investigating spectrum sensing security threats in cognitive radio networks. In: Ad hoc networks. Springer, Cham, pp 60–68

    Chapter  Google Scholar 

  35. Rehman A, Prakash D (2017) Detection of PUE attack in CRN with reduced error in location estimation using novel bat algorithm. Int J Wirel Netw Broadband Technol (IJWNBT) 6(2):1–25

    Article  Google Scholar 

  36. Malhotra M, Aulakh IK (2016) Secure spectrum leasing in cognitive radio networks via secure primary secondary user interaction. In: Artificial intelligence and evolutionary computations in engineering systems. Springer, New Delhi, pp 735–741

    Google Scholar 

  37. Chaitanya DL, Chari KM (2017) Performance analysis of PUEA and SSDF attacks in cognitive radio networks. In: Computer communication, networking and internet security. Springer, Singapore, pp 219–225

    Google Scholar 

  38. Sultana R, Hussain M (2018) Mitigating primary user emulation attack in cognitive radio network using localization and variance detection. In: Proceedings of first international conference on smart system, innovations and computing. Springer, Singapore, pp 433–444

    Chapter  Google Scholar 

  39. Sharifi M, Sharifi AA, Niya MJM (2018) Cooperative spectrum sensing in the presence of primary user emulation attack in cognitive radio network: multi-level hypotheses test approach. Wirel Netw 24(1):61–68

    Article  Google Scholar 

  40. Marinho J, Granjal J (2015) Monteiro E (2015) A survey on security attacks and countermeasures with primary user detection in cognitive radio networks. EURASIP J Inf Secur 1:4

    Article  Google Scholar 

  41. Zhang J, Cai L, Zhang S (2017) Malicious cognitive user identification algorithm in centralized spectrum sensing system. Future Internet 9(4):79

    Article  Google Scholar 

  42. Pandit S, Singh G (2017) Cognitive radio communication system: spectrum sharing techniques. In: Spectrum sharing in cognitive radio networks. Springer, Cham, pp 1–33

    Chapter  Google Scholar 

  43. Venkatesan KJP, Vijayarangan V (2017) Secure and reliable routing in cognitive radio networks. Wirel Netw 23(6):1689–1696

    Article  Google Scholar 

  44. Bhargavi D, Murthy CR (2010) Performance comparison of energy, matched-filter, and cyclostationarity-based spectrum sensing. In: Eleventh international workshop on signal processing advances in wireless communications (SPAWC). IEEE, pp 1–5

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Muhammad Asif Habib.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirza, M.A., Ahmad, M., Habib, M.A. et al. CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks. J Supercomput 74, 5082–5098 (2018). https://doi.org/10.1007/s11227-018-2378-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2378-6

Keywords

Navigation