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Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing

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Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing. / Qin, Zhijin; Gao, Yue; Plumbley, Mark.

In: IEEE Transactions on Signal Processing, Vol. 66, No. 1, 01.01.2018, p. 5-17.

Research output: Contribution to journalJournal article

Harvard

Qin, Z, Gao, Y & Plumbley, M 2018, 'Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing' IEEE Transactions on Signal Processing, vol 66, no. 1, pp. 5-17. DOI: 10.1109/TSP.2017.2759082

APA

Qin, Z., Gao, Y., & Plumbley, M. (2018). Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing. IEEE Transactions on Signal Processing, 66(1), 5-17. DOI: 10.1109/TSP.2017.2759082

Vancouver

Qin Z, Gao Y, Plumbley M. Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing. IEEE Transactions on Signal Processing. 2018 Jan 1;66(1):5-17. Available from, DOI: 10.1109/TSP.2017.2759082

Author

Qin, Zhijin; Gao, Yue; Plumbley, Mark / Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing.

In: IEEE Transactions on Signal Processing, Vol. 66, No. 1, 01.01.2018, p. 5-17.

Research output: Contribution to journalJournal article

Bibtex

@article{dc31d38d6bf14c3c8aa7cd3356ef6b7e,
title = "Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing",
author = "Zhijin Qin and Yue Gao and Mark Plumbley",
year = "2018",
month = "1",
doi = "10.1109/TSP.2017.2759082",
volume = "66",
pages = "5--17",
journal = "IEEE Transactions on Signal Processing",
issn = "1053-587X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Malicious User Detection based on Low-Rank Matrix Completion in Wideband Spectrum Sensing

AU - Qin,Zhijin

AU - Gao,Yue

AU - Plumbley,Mark

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In cognitive radio networks, cooperative spectrum sensing (CSS) has been a promising approach to improve sensing performance by utilizing spatial diversity of participating secondary users (SUs). In current CSS networks, all cooperative SUs are assumed to be honest and genuine. However, the presence of malicious users sending out dishonest data can severely degrade the performance of CSS networks. In this paper, a framework with high detection accuracy and low costs of data acquisition at SUs is developed, with the purpose of mitigating the influences of malicious users. More specifically, a low-rank matrix completion based malicious user detection framework is proposed. In the proposed framework, in order to avoid requiring any prior information about the CSS network, a rank estimation algorithm and an estimation strategy for the number of corrupted channels are proposed. Numerical results show that the proposed malicious user detection framework achieves high detection accuracy with lower data acquisition costs in comparison with the conventional approach. After being validated by simulations, the proposed malicious user detection framework is tested on the real-world signals over TV white space spectrum.

AB - In cognitive radio networks, cooperative spectrum sensing (CSS) has been a promising approach to improve sensing performance by utilizing spatial diversity of participating secondary users (SUs). In current CSS networks, all cooperative SUs are assumed to be honest and genuine. However, the presence of malicious users sending out dishonest data can severely degrade the performance of CSS networks. In this paper, a framework with high detection accuracy and low costs of data acquisition at SUs is developed, with the purpose of mitigating the influences of malicious users. More specifically, a low-rank matrix completion based malicious user detection framework is proposed. In the proposed framework, in order to avoid requiring any prior information about the CSS network, a rank estimation algorithm and an estimation strategy for the number of corrupted channels are proposed. Numerical results show that the proposed malicious user detection framework achieves high detection accuracy with lower data acquisition costs in comparison with the conventional approach. After being validated by simulations, the proposed malicious user detection framework is tested on the real-world signals over TV white space spectrum.

U2 - 10.1109/TSP.2017.2759082

DO - 10.1109/TSP.2017.2759082

M3 - Journal article

VL - 66

SP - 5

EP - 17

JO - IEEE Transactions on Signal Processing

T2 - IEEE Transactions on Signal Processing

JF - IEEE Transactions on Signal Processing

SN - 1053-587X

IS - 1

ER -