Abstract
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.
Highlights
S PECTRUM sensing, a promising solution to identify potential spectral holes, is one of the most challenging tasks in cognitive radio (CR) networks [1]
Exact Matrix completion (MC) cannot be guaranteed if Kmax is much smaller than K, or extra data acquisition costs are caused if Kmax is much larger than K
We proposed a low-rank matrix completion (MC) based malicious user detection framework for the secure cooperative spectrum sensing (CSS) networks
Summary
S PECTRUM sensing, a promising solution to identify potential spectral holes, is one of the most challenging tasks in cognitive radio (CR) networks [1]. Due to the openness of low-layer protocol stacks, CSS networks are vulnerable to attacks from spectrum sensing data. In CSS networks, SUs that launch SSDF attackers are called malicious users. In decentralized CSS networks, sensing results are exchanged between neighboring SUs to improve the network reliability to link failure This characteristic makes decentralized CSS more vulnerable to malicious attacks [6], as the observations at honest SUs are available to malicious users during the information exchanging and convergence process. Along with improving the security of CSS networks through malicious user detection, another key challenge in attempting to build CSS networks comes from the need to reduce data acquisition costs at SUs. Due to the well-known Nyquist sampling theorem, sampling rates should be no less than twice the signal bandwidth. The data acquisition costs are significantly reduced at SUs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
