Malicious User Detection in a Cognitive Radio Cooperative Sensing System
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In this article, the authors investigate schemes to identify the malicious users based on outlier detection techniques for a cooperative sensing system employing energy detection at the sensors, considering constraints imposed by the CR scenario such as the lack of information about the primary signal propagation environment and the small size of the sensing data samples.Abstract:
Reliable detection of primary users (PUs) is an important task for cognitive radio (CR) systems. Cooperation among a few spectrum sensors has been shown to offer significant gain in the performance of the CR spectrum-sensing system by countering the shadow-fading effects. We consider a parallel fusion network in which the sensors send their sensing information to an access point which makes the final decision regarding presence or absence of the PU signal. It has been shown in the literature that the presence of malicious users sending false sensing data can severely degrade the performance of such a cooperative sensing system. In this paper, we investigate schemes to identify the malicious users based on outlier detection techniques for a cooperative sensing system employing energy detection at the sensors. We take into consideration constraints imposed by the CR scenario such as the lack of information about the primary signal propagation environment and the small size of the sensing data samples. Considering partial information of the PU activity, we propose a novel method to identify the malicious users. We further propose malicious user detection schemes that take into consideration the spatial information of the CR sensors. The performance of the proposed schemes are studied using simulations.read more
Citations
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Proceedings ArticleDOI
Distributed cooperative spectrum sensingwith double-topology
TL;DR: Results show that the proposed double-topology scheme suppresses redundancy and offers similar performance when compared to the case of independent node contributions.
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Low-cost Influence-Limiting Defense against Adversarial Machine Learning Attacks in Cooperative Spectrum Sensing
TL;DR: In this article, a low-cost version of the influence-limiting defense is proposed, which can decrease the computation cost significantly (the time cost is reduced to less than 20% of the original defense) while still maintaining the same level of defense performance.
Book ChapterDOI
Malicious User Detection in Cooperative Sensing Environment Using Robust Distance
TL;DR: The robust distance based on minimum covariance determinant (MCD) is used to identify the malicious users in the network without assuming an upper bound on the number of such users in a network.
Collaboration based spectrum sharing algorithms in cognitive radio networks
TL;DR: This dissertation addresses the spectrum sensing data falsification (SSDF) attack in IEEE 802.22 wireless regional area network (WRAN) under the sensing based collaboration model, and presents an adaptive reputation based clustering algorithm to defend against malicious attacks.
Proceedings ArticleDOI
Two-Stage Credit Threshold on Cooperative Spectrum Sensing to Exclude Malicious Users in Mobile Cognitive Radio Networks
TL;DR: Simulation results show that the proposed two-stage credit threshold (TSCT) scheme based CSS can provide with better detection performance regardless of detection rounds, and can work well when MUs outnumber SUs while previous schemes fail.
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