A cooperative spectrum sensing scheme based on the Bayesian reputation model in cognitive radio networks

Zhou, Ming; Shen, Jiafeng; Chen, Huifang; Xie, Lei

doi:10.1109/wcnc.2013.6554634

Cited by 14 publications

(4 citation statements)

References 13 publications

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“…• Using prioritized sequential probability ratio test [29], and fine-grained reputation management [30], to enable robust data fusion.…”

Section: Overviewmentioning

confidence: 99%

“…In fact, for all the possible pairs of users malicious and non-malicious, we calculate the probability of giving the malicious users bigger reputation than the normal user. This defined error probability of estimated weight is plotted as a function of the number of iterations where after each 10 iterations, we change the number of malicious users (30,60, and 90 out of 195). First, that we can remark that during the first 10 iterations (10 malicious users), IPMCD was not able to distinguish the malicious users (P W = 0.5) since they have no sufficient number of groups nor iterations (0.5 for IPMCD and 0.36 for IPMCD-A).…”

Section: Simulation Setupmentioning

confidence: 99%

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Location privacy preservation in secure crowdsourcing-based cooperative spectrum sensing

Chkirbene

Hasna

Hamila

et al. 2016

J Wireless Com Network

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Spectrum sensing is one of the most essential components of cognitive radio since it detects whether the spectrum is available or not. However, spectrum sensing accuracy is often degraded due to path loss, interference, and shadowing. Cooperative spectrum sensing (CSS) is one of the proposed solutions to overcome these challenges. It is a key function for dynamic spectrum access that can increase largely the reliability in cognitive radio networks. In fact, several users cooperate to detect the availability of a wireless channel by exploiting spatial diversity. However, cooperative sensing is also facing some series of security threats. In this paper, we focus on two major problems. The first problem is the localization preservation of the secondary users. In fact, malicious users can exploit spatial diversity to localize a secondary user by linking his location-dependent sensing report to his physical position. The existing solutions present a high level of complexity which decreases the performance of the systems. The second problem is the data injection attack, in which malicious CR users may affect the decisions taken by the cognitive users by providing false information, introducing spectrum sensing data falsification (SSDF). In fact, they can submit false sensing reports containing power measurements much larger (or smaller) than the true value to inflate (or deflate) the final average, in which case the fusion center may falsely determine that the channel is busy (or vacant) which increases the false alarm and miss detection probabilities. In this paper, we propose a novel scheme to overcome these problems: iterative per cluster malicious detection (IPCMD). It utilizes applied cryptographic techniques to allow the fusion center (FC) to securely obtain the aggregated result from various secondary users without learning each individual report. IPCMD combines the aggregated sensing reports with their reputation scores during data fusion. The proposed scheme is based on a new algorithm for key generation which can significantly reduce the key management complexity and consequently increase the system performance. Therefore, it can enable secure cooperative spectrum sensing and improve the secondary user location privacy.

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“…• Using prioritized sequential probability ratio test [29], and fine-grained reputation management [30], to enable robust data fusion.…”

Section: Overviewmentioning

confidence: 99%

Section: Simulation Setupmentioning

confidence: 99%

Location privacy preservation in secure crowdsourcing-based cooperative spectrum sensing

Chkirbene

Hasna

Hamila

et al. 2016

J Wireless Com Network

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“…An ordinary SSDF attack is also known as a “Byzantine attack” [ 8 ], and its attack methods mainly include the following: “always yes”, “always no”, “always false”, and “Ffixed probability”. In order to defend against ordinary SSDF attacks, some scholars have conducted corresponding research [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In [ 12 ], a method based on Bayesian detection was used to defend against SSDF attacks, but it needs to detect a fixed number of nodes, which consumes a large amount of energy on the system.…”

Section: Introductionmentioning

confidence: 99%

“…In order to defend against ordinary SSDF attacks, some scholars have conducted corresponding research [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In [ 12 ], a method based on Bayesian detection was used to defend against SSDF attacks, but it needs to detect a fixed number of nodes, which consumes a large amount of energy on the system. Lu et al explained that when a system is attacked by SSDF independently or jointly launched by MUs [ 13 ], it can defend against malicious users (MUs) with the help of trusted SUs, that is, by only relying on the data of trusted SUs during data fusion, it can find MUs.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing

Xie

Zhou

Gao

2023

Sensors

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In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.

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2023

5G and Beyond Wireless Communication Networks

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A cooperative spectrum sensing scheme based on the Bayesian reputation model in cognitive radio networks

Cited by 14 publications

References 13 publications

Location privacy preservation in secure crowdsourcing-based cooperative spectrum sensing

Location privacy preservation in secure crowdsourcing-based cooperative spectrum sensing

Adaptive Trust Threshold Model Based on Reinforcement Learning in Cooperative Spectrum Sensing

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