An Effective and Optimal Fusion Rule in the Presence of Probabilistic Spectrum Sensing Data Falsification Attack

Document Type : Research Paper

Author

Department of Electrical Engineering, University of Bonab, Bonab, Iran

Abstract

Cognitive radio (CR) network is an excellent solution to the spectrum scarcity problem. Cooperative spectrum sensing (CSS) has been widely used to precisely detect of primary user (PU) signals. The trustworthiness of the CSS is vulnerable to spectrum sensing data falsification (SSDF) attack. In an SSDF attack, some malicious users intentionally report wrong sensing results to cheat the fusion center (FC) and disturb the FC’s global decision on the PU activity. In this paper, we introduce an effective data fusion rule called attack-aware optimal voting rule (AOVR) to confront the SSDF attack in the CSS procedure. In the beginning stages of the cooperative sensing, two important SSDF attack parameters are estimated and then applied in a conventional voting rule to acquire an optimal number of CR users to minimize the global error probability. Two estimated attack parameters include the probabilities of attack in both occupied and empty frequency bands. Simulation results confirm that the proposed attack-aware approach achieves very good performance over the existing conventional cooperative sensing methods.

Keywords

References

[1]     J. Mitola and G.Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Personal Communication, vol. 6, no. 4, pp. 13-18, August 1999.
[2]     S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, Feb. 2005.
[3]     I.F. Akyildiz, W.Y. Lee, M.C. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, no. 13, pp. 2127-2159, Sept. 2006.
[4]     F.F. Digham, M.-S. Alouini, and M. Simon, “On the energy detection of unknown signals over fading channels,” Proceedings of the IEEE International Conference on Communications, vol. 5, pp. 3575-9, May 2003.
[5]     S.M. Mishra, A. Sahai, and R.W. Brodersen, “Cooperative sensing among cognitive radios,” Proceedings of the IEEE International Conference on Communications, pp. 1658-1663, June 2006.
[6]     I.F. Akyildiz, B.F. Lo, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: A survey,” Physical Communication, vol. 40, no. 1, pp. 40-62, March 2011.
[7]     J. Ma, G. Zhao, and Y. Li, “Soft combining and detection for cooperative spectrum sensing in cognitive radio networks,” IEEE Transaction on Wireless Communications, vol. 7, no. 11, pp. 4502-4507, Nov. 2008.
[8]     R. Chen, J. M. Park, Y. T. Hou and J. H. Reed, “Toward secure distributed spectrum sensing in cognitive radio networks,” IEEE Communications Magazine, vol. 46, no. 4, pp. 50-55, April 2008.
[9]     R. Chen, J. Park, and K. Bian, “Robustness against Byzantine failures in distributed spectrum sensing,” Computer Communication, vol. 35, no. 17, pp. 2115-2124, Oct. 2012.
[10]  C.Y. Chen, Y.H. Chou, H.C. Chao, and C.H. Lo, “Secure centralized spectrum sensing for cognitive radio networks,” Wireless Networks, vol. 18, no. 6, pp. 667-677, March 2012.
[11]  V. Chen, M. Song, and C. Xin, “CoPD: a conjugate prior based detection scheme to countermeasure spectrum sensing data falsification attacks in cognitive radio networks,” Wireless Networks, vol. 20, no. 8, pp. 2521-2528, Nov. 2014.
[12]  J. C. Clement, “Jettison the defectives: a robust cooperative spectrum sensing scheme in a cognitive radio networks,” Circuits Syst Signal Process, DOI 10.1007/s00034-017-0672-9, Sept. 2017.
[13]  A.A. Sharifi and M. J. Musevi Niya, “Defense against SSDF attack in cognitive radio networks: attack-aware collaborative spectrum sensing approach,” IEEE Communications Letters, vol. 20, no. 1, pp. 93-96, Jan. 2016.
[14]  A.A. Sharifi and J. Musevi Niya, “Securing collaborative spectrum sensing against malicious attackers in cognitive radio networks,” Wireless Personal Communications, vol. 90, no. 1, pp. 75-91, Sept. 2016.
[15]  A.A. Sharifi, M. Sharifi, and J. Musevi Niya, “Reputation-based likelihood ratio test with anchor nodes assistance,” 8th Internationa Symposium on Telecommunications, Tehran, Sept. 2016.
[16]  A.G. Fragkiadakis, E. Z. Tragos, and I. G. Askoxylakis, “A survey on security threats and detection techniques in cognitive radio networks,” IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 428-445, First Quarter 2013.
[17]  L. Zhang, G. Ding, Q. Wu, Y. Zou, Z. Han, and J. Wang, “Byzantine attack and defense in cognitive radio networks: a survey,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1342-1363, Third Quarter 2015.
[18]  P.K. Varshney, Distributed detection and data fusion, Springer-Verlag, 1997.
[19]  T.S. Rappaport, Wireless communications: Principles and Practice, Prentice Hall, 1996.
[20]  A.A. Sharifi and M. Mofarreh-Bonab, “Spectrum sensing data falsification attack in cognitive radio networks: an analytical model for evaluation and mitigation of performance degradation,” AUT Journal of Electrical Engineering, vol. 50, no. 1, pp. 43-50, Winter & Spring 2018.

Files

Share

How to cite

Statistics