Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework

XU Zhenyu; SUN Zhiguo; GUO Lili; Muhammad Zahid Hammad; Chintha Tellambura

doi:10.1049/cje.2021.00.090

Volume 31 Issue 2

Mar. 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(2): 240-254

XU Zhenyu, SUN Zhiguo, GUO Lili, et al., “Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework,” Chinese Journal of Electronics, vol. 31, no. 2, pp. 240-254, 2022, doi: 10.1049/cje.2021.00.090

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XU Zhenyu, SUN Zhiguo, GUO Lili, et al., “Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework,” Chinese Journal of Electronics, vol. 31, no. 2, pp. 240-254, 2022, doi: 10.1049/cje.2021.00.090

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Joint Spectrum Sensing and Spectrum Access for Defending Massive SSDF Attacks: A Novel Defense Framework

doi: 10.1049/cje.2021.00.090

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G1H9, Canada

Funds: This work was supported in part by the Fundamental Research Funds for the Central Universities (3072021CF0809) and National Natural Science Foundation of China (62001138)

More Information

Author Bio:
received the B.E. degree from China University of Geosciences in 2015. He is a Ph.D. candidate of College of Information and Communication, Harbin Engineering University. His research interests include cognitive radio and intelligent reflection surface.(Email: xu4zhenyu@gmail.com)

(corresponding author) received the Ph.D. degree from Harbin Engineering University in 2005. He is a Professor in College of Information and Communication, Harbin Engineering University. His research interests include cognitive communication and antiinterference technology.(Email: sunzhiguo@hrbeu.edu.cn)

received the Ph.D. degree from Harbin Engineering University in 2005. He is a Professor in College of Information and Communication, Harbin Engineering University. His research interests include efficient communication and signal detection.(Email: guolili@hrbeu.edu.cn)

is a Ph.D. candidate of College of Information and Communication, Harbin Engineering University. His research interests include signal detection and multi-sensors data fusion.(Email: mzhammad@hrbeu.edu.cn)

received the Ph.D. degree from the University of Victoria, Canada, in 1993. He is a Professor with the Department of Electrical and Computer Engineering at the University of Alberta. His research interests include NOMA, massive MIMO, 5G and future wireless systems.(Email: chintha@ece.ualberta.ca)
Received Date: 2021-03-10
Accepted Date: 2021-08-20

Available Online: 2021-12-01

Publish Date: 2022-03-05

Abstract

Abstract

Multiple secondary users (SUs) perform collaborative spectrum sensing (CSS) in cognitive radio networks to improve the sensing performance. However, this system severely degrades with spectrum sensing data falsification (SSDF) attacks from a large number of malicious secondary users, i.e., massive SSDF attacks. To mitigate such attacks, we propose a joint spectrum sensing and spectrum access framework. During spectrum sensing, each SU compares the decisions of CSS and independent spectrum sensing (IndSS), and then the reliable decisions are adopted as its final decisions. Since the transmission slot is divided into several tiny slots, at the stage of spectrum access, each SU is assigned with a specific tiny time slot. In accordance with its independent final spectrum decisions, each node separately accesses the tiny time slot. Simulation results verify effectiveness of the proposed algorithm.
- Collaborative spectrum sensing,
- Spectrum sensing data falsification,
- Spectrum access,
- Generalized likelihood ratio test

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References(42)

References

[1]	A. Ghasemi and E. S. Sousa, “Spectrum sensing in cognitive radio networks: Requirements, challenges and design trade-offs,” IEEE Communications Magazine, vol.46, no.4, pp.32–39, 2008. doi: 10.1109/MCOM.2008.4481338
[2]	M. Marcus, J. Burtle, B. Franca, et al., “Federal communications commission spectrum policy task force,” Report of the Unlicensed Devices and Experimental Licenses Working Group, No.02-135, 2002.
[3]	F. Yan, J. Zhao, H. Qu, and X. Xu, “Energy-efficient cooperative strategy in RF energy harvesting cognitive radio network,” Chinese Journal of Electronics, vol.28, no.3, pp.651–656, 2019. doi: 10.1049/cje.2019.03.002
[4]	K. Wu, M. Tang, C. Tellambura, et al., “Cooperative spectrum sensing as image segmentation: A new data fusion scheme,” IEEE Communications Magazine, vol.56, no.4, pp.142–148, 2018. doi: 10.1109/MCOM.2018.1700743
[5]	L. Zhang, T. Song, M. Wu, et al., “Traffic-adaptive proactive spectrum handoff strategy for graded secondary users in cognitive radio networks,” Chinese Journal of Electronics, vol.24, no.4, pp.844–851, 2015. doi: 10.1049/cje.2015.10.030
[6]	Z. Sun, Z. Xu, M. Z. Hammad, et al., “Defending against massive ssdf attacks from a novel perspective of honest secondary users,” IEEE Communications Letters, vol.23, no.10, pp.1696–1699, 2019.
[7]	A. Ali and W. Hamouda, “Advances on spectrum sensing for cognitive radio networks: Theory and applications,” IEEE Communications Surveys & Tutorials, vol.19, no.2, pp.1277–1304, 2016.
[8]	H. Chen, M. Zhou, L. Xie, et al., “Cooperative spectrum sensing with m-ary quantized data in cognitive radio networks under SSDF attacks,” IEEE Transactions on Wireless Communications, vol.16, no.8, pp.5244–5257, 2017. doi: 10.1109/TWC.2017.2707407
[9]	Y. Fu and Z. He, “Bayesian-inference-based sliding window trust model against probabilistic SSDF attack in cognitive radio networks,” IEEE Systems Journal, vol.14, no.2, pp.1764–1775, 2020.
[10]	S. Zhang, Y. Wang, P. Wan, et al., “Clustering algorithm-based data fusion scheme for robust cooperative spectrum sensing,” IEEE Access, vol.8, pp.5777–5786, 2020.
[11]	Q. Pei, H. Li, and X. Liu, “Neighbor detection-based spectrum sensing algorithm in distributed cognitive radio networks,” Chinese Journal of Electronics, vol.26, no.2, pp.399–406, 2017. doi: 10.1049/cje.2016.06.018
[12]	J. Wu, Y. Yu, H. Zhu, et al., “Cost-benefit tradeoff of byzantine attack in cooperative spectrum sensing,” IEEE Systems Journal, vol.14, no.2, pp.2532–2543, 2020.
[13]	C. S. Hyder, B. Grebur, L. Xiao, et al., “ARC: Adaptive reputation based clustering against spectrum sensing data falsification attacks,” IEEE Transactions on Mobile Computing, vol.13, no.8, pp.1707–1719, 2014. doi: 10.1109/TMC.2013.26
[14]	J. Feng, S. Li, S. Lv, et al., “Securing cooperative spectrum sensing against collusive false feedback attack in cognitive radio networks,” IEEE Transactions on Vehicular Technology, vol.67, no.9, pp.8276–8287, 2018. doi: 10.1109/TVT.2018.2841362
[15]	J. Wang, R. Chen, J. J. Tsai, et al., “Trust-based mechanism design for cooperative spectrum sensing in cognitive radio networks,” Computer Communications, vol.116, pp.90–100, 2018. doi: 10.1016/j.comcom.2017.11.010
[16]	J. Wu, Y. Yu, T. Song, et al., “Robust reputation management mechanism in cooperative spectrum sensing,” Electronics Letters, vol.55, no.21, pp.1128–1130, 2019. doi: 10.1049/el.2019.2302
[17]	J. Wu, T. Song, Y. Yu, et al., “Reuse of byzantine data in cooperative spectrum sensing using sequential detection,” IET Communications, vol.14, no.2, pp.251–261, 2020.
[18]	X. He, H. Dai, and P. Ning, “A byzantine attack defender in cognitive radio networks: The conditional frequency check,” IEEE Transactions on Wireless Communications, vol.12, no.5, pp.2512–2523, 2013. doi: 10.1109/TWC.2013.031313.121551
[19]	Y. Al-Mathehaji, S. Boussakta, M. Johnston, et al., “Defeating SSDF attacks with trusted nodes assistance in cognitive radio networks,” IEEE Sensors Letters, vol.1, no.4, pp.1–4, 2017.
[20]	S. Althunibat, B. J. Denise, and F. Granelli, “Identification and punishment policies for spectrum sensing data falsification attackers using delivery-based assessment,” IEEE Transactions on Vehicular Technology, vol.65, no.9, pp.7308–7321, 2015.
[21]	L. Zhang, G. Ding, Q. Wu, et al., “Defending against byzantine attack in cooperative spectrum sensing: Defense reference and performance analysis,” IEEE Access, vol.4, pp.4011–4024, 2016. doi: 10.1109/ACCESS.2016.2593952
[22]	S. Althunibat, M. Di Renzo, and F. Granelli, “Robust algorithm against spectrum sensing data falsification attack in cognitive radio networks,” 2014 IEEE 79th Vehicular Technology Conference (VTC Spring), IEEE, Seoul, Korea (South), pp.1–5, 2014.
[23]	L. Zhang, G. Nie, G. Ding, Q. Wu, et al., “Byzantine attacker identification in collaborative spectrum sensing: A robust defense framework,” IEEE Transactions on Mobile Computing, vol.18, no.9, pp.1992–2004, 2019.
[24]	M. Jo, L. Han, D. Kim, et al., “Selfish attacks and detection in cognitive radio ad-hoc networks,” IEEE Network, vol.27, no.3, pp.46–50, 2013. doi: 10.1109/MNET.2013.6523808
[25]	A. S. Rawat, P. Anand, H. Chen, et al., “Collaborative spectrum sensing in the presence of byzantine attacks in cognitive radio networks,” IEEE Transactions on Signal Processing, vol.59, no.2, pp.774–786, 2011. doi: 10.1109/TSP.2010.2091277
[26]	R. Chen, J. M. Park, and K. Bian, “Robust distributed spectrum sensing in cognitive radio networks,” IEEE INFOCOM 2008-The 27th Conference on Computer Communications, Phoenix, AZ, USA, pp.1876–1884, 2008.
[27]	Y. He, F. R. Yu, Z. Wei, et al., “Trust management for secure cognitive radio vehicular ad hoc networks,” Ad Hoc Networks, vol.86, pp.154–165, 2019. doi: 10.1016/j.adhoc.2018.11.006
[28]	Y. Zhao, M. Song, and C. Xin, “A weighted cooperative spectrum sensing framework for infrastructure-based cognitive radio networks,” Computer Communications, vol.34, no.12, pp.1510–1517, 2011. doi: 10.1016/j.comcom.2011.02.007
[29]	A. A. Sharifi and M. J. M. 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, 2015.
[30]	L. Ma, Y. Xiang, Q. Pei, et al., “Robust reputation-based cooperative spectrum sensing via imperfect common control channel,” IEEE Transactions on Vehicular Technology, vol.67, no.5, pp.3950–3963, 2018. doi: 10.1109/TVT.2017.2763980
[31]	X. Ling, B. Wu, H. Wen, et al., “Adaptive threshold control for energy detection based spectrum sensing in cognitive radios,” IEEE Wireless Communications Letters, vol.1, no.5, pp.448–451, 2012. doi: 10.1109/WCL.2012.062512.120299
[32]	D. M. M. Plata and Á. G. A. Reátiga, “Evaluation of energy detection for spectrum sensing based on the dynamic selection of detection-threshold,” Procedia Engineering, vol.35, pp.135–143, 2012. doi: 10.1016/j.proeng.2012.04.174
[33]	B. Sarala, S. R. Devi, and J. J. J. Sheela, “Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method,” Computer Communications, vol.152, pp.1–7, 2020. doi: 10.1016/j.comcom.2019.12.058
[34]	W. Zhang, R. K. Mallik, and K. B. Letaief, “Cooperative spectrum sensing optimization in cognitive radio networks,” 2008 IEEE International Conference on Communications, Beijing, China, pp.3411–3415, 2008.
[35]	W. Zhang, R. K. Mallik, and K. B. Letaief, “Optimization of cooperative spectrum sensing with energy detection in cognitive radio networks,” IEEE Transactions on Wireless Communications, vol.8, no.12, pp.5761–5766, 2009. doi: 10.1109/TWC.2009.12.081710
[36]	Y. Liu, C. Zeng, H. Wang, et al., “Energy detection threshold optimization for cooperative spectrum sensing,” 2010 2nd International Conference on Advanced Computer Control, IEEE, Shenyang, China, vol.4, pp.566–570, 2010.
[37]	W. Saad, Z. Han, T. Basar, et al., “Coalition formation games for collaborative spectrum sensing,” IEEE Transactions on Vehicular Technology, vol.60, no.1, pp.276–297, 2010.
[38]	Y. Zou, Y. D. Yao, and B. Zheng, “Outage probability analysis of cognitive transmissions: Impact of spectrum sensing overhead,” IEEE Transactions on Wireless Communications, vol.9, no.8, pp.2676–2688, 2010. doi: 10.1109/TWC.2010.061710.100108
[39]	C. E. Shannon, “A mathematical theory of communication,” Bell System Technical Journal, vol.27, no.3, pp.379–423, 1948. doi: 10.1002/j.1538-7305.1948.tb01338.x
[40]	F. Gao, L. Guo, H. Li, et al., “Quantizer design for distributed GLRT detection of weak signal in wireless sensor networks,” IEEE Transactions on Wireless Communications, vol.14, no.4, pp.2032–2042, 2014.
[41]	J. Fang, Y. Liu, H. Li, et al., “One-bit quantizer design for multisensor glrt fusion,” IEEE Signal Processing Letters, vol.20, no.3, pp.257–260, 2013. doi: 10.1109/LSP.2013.2243144
[42]	S. M. Kay, Fundamentals of Statistical Signal Processing, Prentice Hall PTR, 1993.