Low-rank matrix completion based malicious user detection in cooperative spectrum sensing

Abstract

Abstract-In a cognitive radio (CR) system, cooperative spectrum sensing (CSS) is the key to improving sensing performance in deep fading channels. In CSS networks, signals received at the secondary users (SUs) are sent to a fusion center to make a final decision of the spectrum occupancy. In this process, the presence of malicious users sending false sensing samples can severely degrade the performance of the CSS network. In this paper, with the compressive sensing (CS) technique being implemented at each SU, we build a CSS network with double sparsity property. A new malicious user detection scheme is proposed by utilizing the adaptive outlier pursuit (AOP) based low-rank matrix completion in the CSS network. In the proposed scheme, the malicious users are removed in the process of signal recovery at the fusion center. The numerical analysis of the proposed scheme is carried out and compared with an existing malicious user detection algorithm.

Securing Cognitive Radio Networks using blockchains

“…Quality of data transmission and weights combine to poll vote. Similarly in the article [24], compressed sensing technique is presented in which MU is removed while signal is being processed at fusion center. Low ranked matrix completion mechanism is applied for this purpose.…”

Secure Cooperative Spectrum Sensing Strategy Based on Reputation Mechanism for Cognitive Wireless Sensor Networks

“…Ghaznavi and Jamshidi [13] proposed a fast search algorithm based on the clustering network structure to detect the malicious nodes of each cluster, which can reduce the overhead by reducing the sensing information exchange between the cognitive users and the FC. By constructing a cooperative sensing network with double sparsity property, Qin et al [14] introduced a compressive sensing technique and the strategy of adaptive outlier pursuit with low-rank matrix completion to detect malicious users. By utilizing the spatial characteristics of the CR sensors, Kaligineedi et al [15] proposed an outlier detection technique with constraints of small size of the sensing data samples.…”

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

“…As well as the existing work on malicious user detection, MC-based CSS networks have been studied [12], [24]- [29], with the purpose of alleviating the costs of data acquisition at SUs. Meng et al [24] introduced the concept of MC to CSS networks.…”

“…By utilizing the information from the geo-location database, a prior information assisted wideband spectrum sensing algorithm was proposed in [28], with the purpose of improving the detection performance and reducing the costs of data acquisition at SUs. Additionally, Qin et al [29] proposed to remove the corrupted samples at the FC with sub-Nyquist sampling rates at SUs. However, rank of the matrix at the FC and malicious user number are assumed to be known in advance in the considered CSS networks.…”

“…Another motivation of our work comes from the need to reduce data acquisition costs at SUs without loss of any information. The aforementioned MC-based CSS methods [12], [24]- [26], [28], [29] focus on reducing the costs of data acquisition at SUs without considering any security issues or require high data transmission cost in CSS networks. Therefore, a malicious user detection algorithm with energy efficiency at SUs is strongly needed, but challenging.…”