Research ArticleOpen AccessGoogle Scholar indexed
A New Method for Sensing Cognitive Radio Network under Malicious Attacker
Department of Telecommunication, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Telecommunication, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
- 1 Department of Telecommunication, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
- 2 Department of Telecommunication, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
International Journal of Communications, Network and System Sciences·Volume 06 (2013)·Pages 60–65·Published 29 January 2013·DOI10.4236/ijcns.2013.61007
Copy link · social · email
Abstract
Cognitive radio has been designed for solving the problem of spectrum scarcity by using the spectrum of primary users who don’t use their spectrum on that time. For sensing the spectrum, collaborative spectrum sensing has been utilized because of robustness. In this paper, a new collaborative spectrum method is proposed based on Least Mean Square (LMS) algorithm. In this scheme, the weights of secondary users were updated in time and finally the sensing results were combined in the fusion center based on their trusted weights. Simulation results show that the proposed scheme can significantly reduce the effects of Spectrum Sensing Data Falsification (SSDF) attackers, when they are smart malicious, and even percentage of malicious users are more than trusted users.
KeywordsCognitive RadioLMS AlgorithmFusion CenterMalicious UserSSDF Attack
- T. Y?cek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Communications Surveys & Tutorials, Vol. 11, No. 1, 2009, pp. 116-130. doi:10.1109/SURV.2009.090109
- S. Y. Xu, Z. J. Zhao and J. Shang, “Spectrum Sensing Based on Cyclostationary,” IEEE Workshop on Power Electronics and Intelligent Transportation System, 2-3 August 2008, pp. 171-174. doi:10.1109/PEITS.2008.41
- S. Chantaraskul and K. Moessner, “Implementation of Wavelet Analysis for Spectrum Opportunity Detection,” IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo, 13-16 September 2009, pp. 2310-2314.
- Y. Zeng and Y.-C. Liang, “Covariance Based Signal Detections for Cognitive Radio,” 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Dublin, 17-20 April 2007, pp. 202-207.
- Y. Zhang, G. C. Xu and X. Z. Geng, “Security Threats in Cognitive Radio Networks,” 10th IEEE International Conference on High Performance Computing and Communications, Dalian, 25-27 September 2008, pp. 1036-1041. doi:10.1109/HPCC.2008.21
- W. F. Wang “Denial of Service Attacks in Cognitive Radio Networks,” 2010 International Conference on Environmental Science and Information Application Technology (ESIAT), Wuhan, 17-18 July 2010, pp. 530-535.
- R. Chen, J. M. Park and J. H. Reed, “Defense against Primary User Emulation Attacks in Cognitive Radio Networks,” IEEE Journal on Selected Areas in Communications, Vol. 26, No. 1, 2008, pp. 25-37. doi:10.1109/JSAC.2008.080104
- R. Chen, J.-M. Park and K. Bian, “Robust Distributed Spectrum Sensing in Cognitive Radio Networks,” IEEE Proceedings of the 27th Conference on Computer Communications, 13-18 April 2008, pp. 1876-1884.
- J. Ma, G. Zhao and Y. Li, “Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks,” IEEE Transactions on Wireless Communications, Vol. 7, No. 11, 2008, pp. 4502-4507. doi:10.1109/T-WC.2008.070941
- Y. Zeng, Y. Liang, A. Hoang and R. Zhang, “A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions,” EURASIP Journal on Advances in Signal Processing, Vol. 2010, 2010, p. 2. doi:10.1155/2010/381465
- S. Ciftci and M. Torlak, “A Comparison of Energy Detectability Models for Cognitive Radios in Fading Environments,” Wireless Personal Communications, Vol. 68, No. 3, 2013, pp. 553-574.