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Differential Evolution Based Reliable Cooperative Spectrum Sensing in the Presence of Malicious Users

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Abstract

In Cognitive Radio Network, access to the vacant spectrum holes in the licensed Primary User (PU) channel is allowed to the Secondary Users. In order to use the PU spectral holes accurately and on time, spectrum sensing is extremely important to avoid interference with the PU transmission. In Cooperative Spectrum Sensing (CSS) individual users report the Fusion Center (FC) about sensing data for a global decision which leads to better sensing results as compared to non-cooperative sensing. The objective of this paper is to make the CSS performance precise and accurate in the presence of Malicious Users (MUs) reporting false sensing information to the FC. The proposed scheme reduces falsifying effects of the YES, NO and OPPOSITE categories of MUs in CSS using Differential Evolution (DE) algorithm. A dynamic threshold value is determined using optimum coefficient vector by the DE at the FC. This leads to minimum error probability in detecting PU channel when the YES, NO and OPPOSITE categories of MUs reports FC in CSS. The weighting coefficient vector is further employed at the FC to assign weights to the received sensing information of cooperative users. Therefore, cooperative users with minutely erroneous reports receive high weight as compared to MUs. The simulations performed for varying number of MUs, total cooperative users, Signal to Noise Ratios and sensing samples show minimum error in detecting PU activity for the proposed DE technique in comparison with the Kullback–Leibler divergence, particle swarm optimization, Genetic Algorithm and Maximum Gain Combination schemes.

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References

  1. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.

    Google Scholar 

  2. Ghasemi, A., & Sousa, E. S. (2008). Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs. Communications Magazine, IEEE, 46(4), 32–39.

    Google Scholar 

  3. Giupponi, L., Galindo-Serrano, A., Blasco, P., & Dohler, M. (2010). Docitive networks: An emerging paradigm for dynamic spectrum management [dynamic spectrum management]. IEEE Wireless Commun., 17(4), 47–54.

    Google Scholar 

  4. Cabric, D., Mishra, S. M., & Brodersen, R. W. (2004). Implementation issues in spectrum sensing for cognitive radios. In Conference record of the thirty-eighth asilomar conference on signals, systems and computers, 2004 (Vol. 1, pp. 772–776).

  5. Tandra, R., & Sahai, A. (2005). Fundamental limits on detection in low SNR under noise uncertainty. In 2005 international conference on wireless networks, communications and mobile computing (Vol. 1, pp. 464–469).

  6. Hiep, K. V.-V. (2012). A robust cooperative spectrum sensing based on Kullback-Leibler divergence. IEICE Transactions on Communications, E95–B(4), 1286–1290.

    Google Scholar 

  7. Sharifi, A. A., & Niya, J. M. (2016). Securing collaborative spectrum sensing against malicious attackers in cognitive radio networks. Wireless Personal Communications, 90(1), 75–91.

    Google Scholar 

  8. Mehboob, U., Qadir, J., Ali, S., & Vasilakos, A. (2016). Genetic algorithms in wireless networking: Techniques, applications, and issues. Berlin: Springer.

    Google Scholar 

  9. Bhattacharjee, S. (2015). Optimization of probability of false alarm and probability of detection in cognitive radio networks using GA. In Proceedings of ReTIS’15—2nd IEEE international conference on recent trends in information systems, Kolkata, India. https://doi.org/10.1109/retis.2015.7232852.

  10. Akbari, M., & Ghanbarisabagh, M. (2014). A novel evolutionary-based cooperative spectrum sensing mechanism for cognitive radio networks. Wireless Personal Communications, 79(2), 1017–1030. https://doi.org/10.1007/s11277-014-1915-8.

    Article  Google Scholar 

  11. Fathy, M., Tammam, A., & Saafan, A. (2019). Influence of relaying malicious node within cooperative sensing in cognitive radio network. Wireless Network, 5, 2449–2458.

    Google Scholar 

  12. Sharifi, A. A., Sharifi, M., & MuseviNiya, M. J. (2016). Collaborative spectrum sensing under primary user emulation attack in cognitive radio networks. IETE Journal of Research, 62(2), 205–211.

    Google Scholar 

  13. Abrardo, A., Barni, M. Kallas, K., & Tondi, B. (2018). Decision fusion with unbalanced priors under synchronized byzantine attacks : A message-passing approach. In 2018 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC) (pp. 1160–1167).

  14. Gupta, N., Dhurandher, S. K., Member, S., & Sehgal, A. (2019). A contract theory approach-based scheme to encourage secondary users for cooperative sensing in cognitive radio networks. IEEE Systems Journal 1–11.

  15. Cao, X., Lai, L., & Member, S. (2019). Distributed gradient descent algorithm robust to an arbitrary number of byzantine attackers. IEEE Transactions on Signal Processing, 67(22), 5850–5864.

    MathSciNet  Google Scholar 

  16. Zhang, L., Ding, G., & Member, S. (2019). Detecting abnormal power emission for orderly spectrum usage B. generalized likelihood ratio detector with unknown. IEEE Transactions on Vehicular Technology, 68(2), 1989–1992.

    Google Scholar 

  17. Lee, S., Zhang, Y., Yoon, S., & Song, I. (2019). Order statistics and recursive updating with aging factor for cooperative cognitive radio networks under SSDF attacks. ICT Express, 2019, 1–4.

    Google Scholar 

  18. Sun, Z., Xu, Z., Hammad, M. Z., Ning, X., Wang, Q., & Guo, L. (2019). Defending against massive SSDF attacks from a novel perspective of honest secondary users. IEEE Communications Letters, 23(10), 1696–1699.

    Google Scholar 

  19. Grissa, M., Member, S., & Yavuz, A. A. (2017). Preserving the location privacy of secondary users in cooperative spectrum sensing. IEEE Transactions on Information Forensics and Security, 12(2), 418–431.

    Google Scholar 

  20. Grissa, M., Yavuz, A., & Hamdaoui, B. (2016). An efficient technique for protecting location privacy of cooperative spectrum sensing users. In 2016 IEEE conference on computer communications workshops (INFOCOM WKSHPS) (pp. 915–920).

  21. Du, H., Fu, S., & Chu, H. (2015). A credibility-based defense SSDF attacks scheme for the expulsion of malicious users in cognitive radio. International Journal of hybrid information Technology, 8(9), 269–280.

    Google Scholar 

  22. Sharifi, A. A. (2019). An effective and optimal fusion rule in the presence of probabilistic spectrum sensing data falsification attack. Journal of Communication Engineering, 8(1), 78–92.

    Google Scholar 

  23. Chen, H., Zhou, M., Xie, L., & Li, J. (2017). Cooperative spectrum sensing with M-ary quantized data in cognitive radio networks under SSDF attacks. IEEE Transactions on Wireless Communications, 16(8), 5244–5257.

    Google Scholar 

  24. Feng, J., Zhang, M., Xiao, Y., & Yue, H. (2018). Securing cooperative spectrum sensing against collusive SSDF attack using XOR distance analysis in cognitive radio networks. Sensors (Switzerland), 18(2), 1–14.

    Google Scholar 

  25. Feng, J., Li, S., Lv, S., Wang, H., & Fu, A. (2018). Securing cooperative spectrum sensing against collusive false feedback attack in cognitive radio networks. IEEE Transactions on Vehicular Technology, 67(9), 8276–8287.

    Google Scholar 

  26. Fu, Y., & He, Z. (2019). Bayesian-inference-based sliding window trust model against probabilistic SSDF attack. IEEE Systems Journal, 12, 1–12.

    Google Scholar 

  27. Shrivastava, S., John, S., Rajesh, A., & Bora, P. K. (2018). Preventing collusion attacks in cooperative spectrum sensing. In International conference on signal processing and communications (SPCOM) (pp. 90–94).

  28. Zhao, F., Li, S., & Feng, J. (2019). Securing cooperative spectrum sensing against DC-SSDF attack using trust fluctuation clustering analysis in cognitive radio networks. Wireless Communications and Mobile Computing, 2019, 1–11.

    Google Scholar 

  29. Wan, R., Ding, L., Xiong, N., & Zhou, X. (2019). Mitigation strategy against spectrum- sensing data falsification attack in cognitive radio sensor networks. International Journal of Distributed Sensor Networks, 15(9), 1–12.

    Google Scholar 

  30. Luo, Z., Zhao, S., Lu, Z., Xu, J., & Sagduyu, Y. E. (2019). When attackers meet AI : Learning-empowered attacks in cooperative spectrum sensing. 1–15.

  31. Gul, N., Qureshi, I. M., Omar, A., Elahi, A., & Khan, M. S. (2017). History based forward and feedback mechanism in cooperative spectrum sensing including malicious users in cognitive radio network. PLoS ONE, 12(8), e0183387.

    Google Scholar 

  32. Gul, N., Qureshi, I. M., Akbar, S., Kamran, M., & Rasool, I. (2018). One-to-many relationship based kullbackLeibler divergence against malicious users in cooperative spectrum sensing. Wireless Communications and Mobile Computing, 2018, 1–14.

    Google Scholar 

  33. Gul, N., Qureshi, I. M., Elahi, A., & Rasool, I. (2018). Defense against malicious users in cooperative spectrum sensing using genetic algorithm. International Journal of Antennas and Propagation, 2018, 1–11.

    Google Scholar 

  34. Gul, N., Naveed, A., Elahi, A., Khattak, T., & Qureshi, I. (2017). A combination of double sided neighbor distance and genetic algorithm in cooperative spectrum sensing against malicious users. In Proceedings of 2017 14th international bhurban conference on applied sciences & technology (IBCAST) (pp. 746–753).

  35. Gul, N., Mansoor, I., Aqdas, Q., Atif, N., & Imtiaz, E. (2019). Secured soft combination schemes against malicious—users in cooperative spectrum sensing. Wireless Personal Communications, 0123456789, 1–20.

    Google Scholar 

  36. Akbari, M., Manesh, M. R., Saleh, A. A., & Ismail, M. (2012). Improved soft fusion based cooperative spectrum sensing using particle swarm optimization. IEICE Electronics Express, 9(6), 436–442.

    Google Scholar 

  37. Quan, Z., Cui, S., & Sayed, A. H. (2008). Optimal linear cooperation for spectrum sensing in cognitive radio network. IEEE Journal of Selected Topics in Signal Processing, 2, 28–40m.

    Google Scholar 

  38. Zainab, A., & Sinha, P. (2016). A survey of cognitive radio reconfigurable antenna design and proposed design using genetic algorithm. In IEEE students’ conference on electrical, electronics and computer science, Bhopal, India (p. 1–6). https://doi.org/10.1109/sceecs.2016.7509263.

  39. Karaboga, D., & Okdem, S. (2004). A simple and global optimization algorithm for engineering problems: differential evolution algorithm. Turkish Journal of Electrical Engineering & Computer Sciences, 12(1), 53–60.

    Google Scholar 

  40. Veterstrom, J., & Thomsen, R. (2004). A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on problems. In IEEE congress on evolutionary computation (pp. 980–987).

  41. Storn, R., & Price, K. V. (1995). Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces. ICSI, USA, Tech. Rep. TR-95-012, 1995 [Online]. Available: http://icsi.berkeley.edu/∼storn/litera.html.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering and Technology, International Islamic University, Islamabad, 44000, Pakistan

    Noor Gul, Muhammad Sajjad Khan & Atif Elahi

  2. Department of Electrical Engineering, Air University, Islamabad, 44000, Pakistan

    Ijaz Mansoor Qureshi

  3. DepartmentofElectronics, University of Peshawar, Peshawar, 25120, Pakistan

    Noor Gul & Sadiq Akbar

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  1. Noor Gul
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  2. Ijaz Mansoor Qureshi
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Correspondence to Noor Gul.

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Gul, N., Qureshi, I.M., Khan, M.S. et al. Differential Evolution Based Reliable Cooperative Spectrum Sensing in the Presence of Malicious Users. Wireless Pers Commun 114, 123–147 (2020). https://doi.org/10.1007/s11277-020-07354-7

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