A Double Adaptive Approach to Tackle Malicious Users in Cognitive Radio Networks

Abstract

Cognitive radio (CR) is being considered as a vital technology to provide solution to spectrum scarcity in next generation network, by efficiently utilizing the vacant spectrum of the licensed users. Cooperative spectrum sensing in cognitive radio network has a promising performance compared to the individual sensing. However, the existence of the malicious users’ attack highly degrades the performance of the cognitive radio networks by sending falsified data also known as spectrum sensing data falsification (SSDF) to the fusion center. In this paper, we propose a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Prior to the double adaptive approach, the maximal ratio combining (MRC) scheme is utilized to assign weight to each user such that the legitimate users experience higher weights than the malicious users. Double adaptive threshold is applied to give a fair chance to the doubtful users to ensure their credibility. A doubtful user that fails the double adaptive threshold test is declared as a malicious user. The results of the legitimate users are combined at the fusion center by utilizing Dempster-Shafer (DS) evidence theory. Effectiveness of the proposed scheme is proved through simulations by comparing with the existing schemes.

A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users

“…In the figure, an FC receives sensing statistics of the PU channel from M users including both normal SUs and MUs. According to the operating criteria of MUs, an AY MU reports higher energy statistics to the FC irrespective of the actual status of the PU channel as if the PU channel is always busy [29]. us, if there exist AY MUs in CSS, the data rate of the secondary system can be severely reduced.…”

“…Various attacks which severely degrade the performance of the CSS have been studied in the CRN. e representative attacks are Byzantine users' attack, jamming attack, and primary user emulation attack (PUEA) [29][30][31][32][33]. e Byzantine users' attack is a type of spectrum sensing data falsification (SSDF) attack, where malicious users (MUs) report false information to the FC.…”

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

“…Feng et al reported the collusive SSDF attack method and proposed a two-level (the request-level and feedback-level) and XDA defence scheme from the perspective of XOR distance analysis to defend against this attack in [24,25], respectively. Khan et al in [26] proposed a double adaptive thresholding technique in order to differentiate legitimate users from doubtful and malicious users. Yuan et al in [27] proposed a maximum mean discrepancy (MMD)-based secure fusion strategy to defend against intelligent SSDF attack for collaborative spectrum sensing in CRNs.…”

Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

“…A multinomial Bayesian trust model is proposed in Bhattacharjee et al (2015) to defend against SSDF attacks by classifying nodes into honest or malicious. A double adaptive threshold technique is proposed in Khan et al (2019) to differentiate the honest and malicious nodes by assigning weights to nodes using a Maximal Ratio Combining (MRC) scheme. Dempster-Shafer evidence theory is used at the FC to combine the decision of all the honest nodes.…”