Abstract
Cooperative spectrum sensing (CSS) is a vital part of cognitive radio networks, which ensures the existence of the primary user (PU) in the network. However, the presence of malicious users (MUs) highly degrades the performance of the system. In the proposed scheme, each secondary user (SU) reports to the fusion center (FC) with a hard decision of the sensing energy to indicate the existence of the PU. The main contribution of this work deals with MU attacks, specifically spectrum sensing data falsification (SSDF) attacks. In this paper, we propose a correlation-based approach to differentiate between the SUs and the outliers by determining the sensing of each SU, and the average value of sensing information with other SUs, to predict the SSDF attack in the system. The FC determines the abnormality of a SU by determining the similarity for each SU with the remaining SUs by following the proposed scheme and declares the SU as an outlier using the box-whisker plot. The effectiveness of the proposed scheme was demonstrated through simulations.
Highlights
The services provided for the rapid growth of the applications, such as computers, laptops, ipads, internet of things (IoT), etc., have increased the demand of the spectrum, which results in spectrum shortage
We proposed a correlation-based approach using the box-whisker plot for the detection of outliers in the networks
We considered the hard decision of each secondary user (SU), and the fusion center (FC) utilized correlation tools and calculated the correlation for finding the similarity of the sensing results of the SUs and outliers
Summary
The services provided for the rapid growth of the applications, such as computers, laptops, ipads, internet of things (IoT), etc., have increased the demand of the spectrum, which results in spectrum shortage. The authors of [20] proposed a neighbor detection-based spectrum sensing algorithm in distributed CRNs, which detects attackers with the help of neighbors during spectrum sensing to improve the decision-making accuracy In this algorithm, the extreme outliers are isolated in the cognitive radio ad hoc network via the modified Z-test, and the q-out-of-m rule is implemented to mitigate the SSDF attack [21]. The authors of [24] utilized a k-medoids clustering algorithm to mine the collection of sensing reports at the FC to determine the attacker’s presence; the proposed scheme can be utilized on streaming data (sensing reports), and thereby detects and isolates the attackers existing in the networks.
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