Abstract
In this chapter, a malicious user detection model is proposed to improve the security of CSS networks. The low-rank MC technique is invoked in the proposed model. More specifically, Sect. 5.1 introduces the related work and main contributions of the work in this chapter. Section 5.2 describes the system model of CSS networks with malicious users. Section 5.3 presents the proposed low-rank MC-based malicious user detection framework along with the proposed rank estimation algorithm and the estimation strategy for the number of malicious users. Section 5.4 shows the numerical analyses of the proposed framework on both simulated and real-world signals. Section 5.5 concludes this chapter.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Physical Communication, 4, 40–62.
Boumal, N., & Absil, P.-A. (2011). RTRMC: A riemannian trust-region method for low-rank matrix completion. In Proceedings of Advances in Neural Information Processing Systems 24 (NIPS) (pp. 406–414)
Candes, E. (2006). Compressive sampling. In Proceedings of the International Congress of Mathematicians, Madrid, Spain (Vol. 3, pp. 1433–1452).
Candes, E., & Recht, B. (2009). Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9, 717–772.
Chen, R., Park, J.-M., & Bian, K. (2008). Robust distributed spectrum sensing in cognitive radio networks. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Phoenix, AZ (pp. 13–18).
Ding, G., Wu, Q., Yao, Y.-D., Wang, J., & Chen, Y. (2013). Kernel-based learning for statistical signal processing in cognitive radio networks: Theoretical foundations, example applications, and future directions. IEEE Signal Processing Magazine, 30, 126–136.
Ghasemi, A., & Sousa, E. (2005). Collaborative spectrum sensing for opportunistic access in fading environments. In Proceedings of the IEEE International Symposium on Dynamic Spectrum Access Networks (DYSPAN), Baltimore, MD (pp. 131 –136).
Kalamkar, S., Banerjee, A., & Roychowdhury, A. (2012). Malicious user suppression for cooperative spectrum sensing in cognitive radio networks using Dixon’s outlier detection method. In Proceedings of the National Conference Communications (NCC), Kharagpur (pp. 1–5).
Kaligineedi, P., Khabbazian, M., & Bhargava, V. (2008). Secure cooperative sensing techniques for cognitive radio systems. In Proceedings of the IEEE International Conference Communications (ICC), Beijing, China (pp. 3406–3410).
Kaligineedi, P., Khabbazian, M., & Bhargava, V. K. (2010). Malicious user detection in a cognitive radio cooperative sensing system. IEEE Transactions on Wireless Communications, 9, 2488–2497.
Li, H. (2010). Reconstructing spectrum occupancies for wideband cognitive radio networks: A matrix completion via belief propagation. In Proceedings of the IEEE International Conference on Communications (ICC), Cape Town, South Africa (pp. 1–6).
Li, H., & Han, Z. (2010). Catch me if you can: An abnormality detection approach for collaborative spectrum sensing in cognitive radio networks. IEEE Transactions on Wireless Communications, 9, 3554–3565.
Liu, Y., Qin, Z., Elkashlan, M., Gao, Y., & Hanzo, L. (2017). Enhancing the physical layer security of non-orthogonal multiple access in large-scale networks. IEEE Transactions on Wireless Communications, 16, 1656–1672.
Ma, Y., Gao, Y., Cavallaro, A., Parini, C. G., Zhang, W., & Liang, Y. C. (2017). Sparsity independent sub-Nyquist rate wideband spectrum sensing on real-time TV white space. IEEE Transactions on Vehicular Technology, 66, 8784–8794.
Ma, Y., Gao, Y., Liang, Y. C., & Cui, S. (2016). Reliable and efficient sub-Nyquist wideband spectrum sensing in cooperative cognitive radio networks. IEEE Journal on Selected Areas in Communications, 34, 2750–2762.
Meng, J., Yin, W., Li, H., Hossain, E., & Han, Z. (2011). Collaborative spectrum sensing from sparse observations in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 29, 327–337.
Qin, Z., Gao, Y., Plumbley, M. D., & Parini, C. G. (2016). Wideband spectrum sensing on real-time signals at sub-Nyquist sampling rates in single and cooperative multiple nodes. IEEE Transactions on Signal Processing, 64, 3106–3117.
RFeye node https://www.crfs.com/all-products/hardware/nodes/.
Tian, Z., & Giannakis, G. (2007). Compressed sensing for wideband cognitive radios. In Proceedings of the IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), Honolulu, HI (pp. 1357–1360).
Wang, W., Chen, L., Shin, K., & Duan, L. (2014). Secure cooperative spectrum sensing and access against intelligent malicious behaviors. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM), Toronto, ON (pp. 1267–1275).
Wang, W., Chen, L., Shin, K., & Duan, L. (2015). Thwarting intelligent malicious behaviors in cooperative spectrum sensing. IEEE Transactions on Mobile Computing, 14, 2392–2405.
Wang, W., Li, H., Sun, Y., & Han, Z. (2009). Attack-proof collaborative spectrum sensing in cognitive radio networks. In Proceedings of the Conference on Information Sciences Systems (CISS) (pp. 130–134).
Wang, W., Li, H., Sun, Y., & Han, Z. (2010). Securing collaborative spectrum sensing against untrustworthy secondary users in cognitive radio networks. EURASIP Journal on Advances in Signal Processing, 2010, 1–15.
Wang, Y., Tian, Z., & Feng, C. (2012a). Collecting detection diversity and complexity gains in cooperative spectrum sensing. IEEE Transactions on Wireless Communications, 11, 2876–2883.
Wang, Y., Tian, Z., & Feng, C. (2012b). Sparsity order estimation and its application in compressive spectrum sensing for cognitive radios. IEEE Transactions on Wireless Communications, 11, 2116–2125.
Yan, M., Yang, Y., & Osher, S. (2013). Exact low-rank matrix completion from sparsely corrupted entries via adaptive outlier pursuit. Journal of Scientific Computing, 56, 433–449.
Yan, Q., Li, M., Jiang, T., Lou, W., & Hou, Y. (2012). Vulnerability and protection for distributed consensus-based spectrum sensing in cognitive radio networks. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) (pp. 900–908).
Zhang, L., Ding, G., Wu, Q., Zou, Y., Han, Z., & Wang, J. (2015). Byzantine attack and defense in cognitive radio networks: A survey. IEEE Communication Surveys and Tutorials, 17, 1342–1363.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Gao, Y., Qin, Z. (2019). Secure Compressive Spectrum Sensing. In: Data-Driven Wireless Networks. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-00290-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-00290-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00289-3
Online ISBN: 978-3-030-00290-9
eBook Packages: EngineeringEngineering (R0)