Reputation-based Likelihood Ratio Test with anchor nodes assistance

Sharifi, Abbas Ali; Sharifi, Morteza; Niya, Javad Musevi

doi:10.1109/istel.2016.7881781

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…By investigating the malicious node's manipulation of sensing result independently or collaboratively, Hyder et al [19] proposed an adaptive reputation-based clustering mechanism, which requires no prior knowledge of distribution of attacking nodes and is applicable for a wide range of attacking scenarios. Focusing on the massive attacks, Sharifi et al [20] made use of Weighted Likelihood Ratio Test to estimate the credit value of each SU. Rawat et al [21] presented a reputation based strategy to identify SSDF attackers.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Yao

Yuan

et al. 2020

IEEE Access

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Due to the potential applicability of spectrum sensing, cognitive wireless sensor networks have attracted plenty of interest in the research community to improve the bandwidth utilization for practical applications. To alleviate the effect of multi-path fading and resolve the problem of hidden terminal, collaborative spectrum sensing (CSS) is regarded as effective technology to obtain better sensing accuracy. However, CSS is usually vulnerable to the attack behaviors originated from malicious sensor nodes. In this paper, an enhanced cooperative spectrum sensing scheme against SSDF attack based on Dempster-Shafer evidence theory for cognitive wireless sensor networks is introduced. First, the holistic credibility of sensor nodes can be evaluated according to the real-time difference between them and the statistical sensing behaviors. Furthermore, the basic probability assignment function can be defined based on evidence theory, and the credibility of sensor nodes can be estimated. Finally, by using the weighted probability assignment for each cognitive sensor node, the fusion center can reduce the influence of malicious sensor nodes on the final decision and ensure the reliability of reports from cooperative sensor nodes. Simulation results demonstrate that the proposed method can resist SSDF attacks significantly and outperform the traditional secure schemes in aspect of sensing accuracy.

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Section: Related Workmentioning

confidence: 99%

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Yao

Yuan

et al. 2020

IEEE Access

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“…The authors utilise geographical and reputational weights to define a two-level FC for secure collaborative sensing. Sharifi et al presented a weighted likelihood ratio rest (WLRT) in [15] to mitigate the effect of Byzantine attack, in which the collaborative weight is calculated by comparing the sensing history of each user with the reliable anchor nodes' global decision. In another work, Usman and Insoo proposed a simple, but the efficient classification method in [16] that classifies the SUs into reliable, neutral and unreliable categories.…”

Section: Related Workmentioning

confidence: 99%

“…Before starting with the usability evaluation, the description and definition of the related parameters are provided. In (15) and 16,…”

Section: Usability Evaluationmentioning

confidence: 99%

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Song

et al. 2020

IET Communications

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“…During SSDF attacks, CR attackers send falsified spectrum sensing results to the FC and disrupt the global sensing decision. To overcome the impact of SSDF attacks, several different solutions have been proposed in previously [5][6][7]. In PUEA, a malicious attacker mimics some characteristics of the legitimate PU's signal.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Threshold Selection Approach in Voting Rule for Detection of Primary User Emulation Attack

Sharifi¹,

Mofarreh-Bonab²

2018

Electrica

Self Cite

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Cognitive radio (CR) technology presents a mechanism for efficient spectrum usage. Spectrum sensing is an essential CR function which includes an intelligent signal processing algorithm to identify the vacant frequency bands. Cooperative spectrum sensing (CSS) has been widely adopted to improve the performance of CR networks. Unfortunately, CR networks are vulnerable to security threats. In this study, we propose an optimal threshold selection approach to address one of the most important attacks called primary user emulation attack (PUEA). In PUEA, a malicious attacker mimics some important primary signal features and deceives CR sensors to prevent them from accessing the available channels. In this study, we assume a malicious PUEA which is relatively located near the potential user (PU) transmitter and senses the spectrum and accurately detects the vacant frequency bands to transmit its fake signal. We estimate attack strength and then apply the K-out-of-N rule to obtain an optimum and dynamic threshold K, minimizing the global error probability. Here, the attack strength is defined as the ratio of the average transmission power of the PUEA to the average power of the PU. The achieved simulation results indicate that the performance of the suggested method is satisfactory in detecting the malicious PUEA compared with conventional methods.

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Reputation-based Likelihood Ratio Test with anchor nodes assistance

Cited by 6 publications

References 14 publications

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

An Enhanced Cooperative Spectrum Sensing Scheme Against SSDF Attack Based on Dempster-Shafer Evidence Theory for Cognitive Wireless Sensor Networks

Reuse of Byzantine data in cooperative spectrum sensing using sequential detection

Dynamic Threshold Selection Approach in Voting Rule for Detection of Primary User Emulation Attack

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