A sensing and etiquette reputation-based trust management for centralized cognitive radio networks

Pei, Qingqi; Yuan, Beibei; Li, Lei; Li, Hongning

doi:10.1016/j.neucom.2012.08.005

Cited by 29 publications

(25 citation statements)

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“…Reference [6] describes the security of cooperative spectrum sensing in distributed CRNs and considers combining the location reliability and malicious intention as a method for trust measurement to increase the success rate of detecting malicious nodes. The double reputation trust management in CRNs is proposed in [7] to govern SUs. The trustiness of SUs based on sensing reputation and etiquette reputation during spectrum sensing and allocation is the evidence of optimizing the data fusion and spectrum allocation policy.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Trust Threshold Value Based on Market Theory in Cognitive Radio Networks

Pei

Wang

2013

2013 5th International Conference on Intelligent Networking and Collaborative Systems

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In Cognitive radio networks (CRNs) the primary users(PUs) and secondary users(SUs) share the spectrum resource, which can be an effective solution to the problem that the existing network spectrum utilization is not high. Therefore, to correctly sense whether the spectrum is idle or not is an important prerequisite for spectrum sharing. Spectrum sensing requires the secondary users to cooperate in sensing and report the sensed data. Therefore the CRN is subject to sensed data tampering and primary user emulation. The trustiness of the SUs is, therefore, very important. In this paper, the trust management is introduced to solve the problem of the trustiness of the SUs. A method of trust threshold value judgment is proposed for selecting the trust threshold in trust management. And the threshold will be a standard of the trustiness of SUs. Market equilibrium in the market theory is adopted. The demand function and supply function are built to find the trust value at the market equilibrium point. With the Beta distribution, the ultimate threshold will be given so that it can ensure the fairness of the SUs.

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Section: Related Workmentioning

confidence: 99%

Dynamic Trust Threshold Value Based on Market Theory in Cognitive Radio Networks

Pei

Wang

2013

2013 5th International Conference on Intelligent Networking and Collaborative Systems

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“…To address the above issues, various secure CSS schemes have been proposed [5][6][7][8][9][10][11][12][13]. When simple attack patterns are adopted by only a few malicious users (MUs) in CRNs, the schemes presented in [5][6][7] can work well enough.…”

Section: Introductionmentioning

confidence: 99%

“…When simple attack patterns are adopted by only a few malicious users (MUs) in CRNs, the schemes presented in [5][6][7] can work well enough. The concept of applying the trust and reputation model in CRNs has also attracted interest recently [8][9][10][11][12]. A reputation-based secure CSS algorithm with trusted node assistance based on [5][6][7] was proposed in [8], which started with trusted SUs merely to assure the inerrability of global decision making.…”

Section: Introductionmentioning

confidence: 99%

“…These two schemes can still work availably even in the presence of a large number of malicious users. Qingqi Pei exploited the cognitive cycle to build the trust model, thus ensuring the security of CSS [10]. The authors in [11] considered the number of false sensing as the attenuation factor of trust to punish MUs; however, they ignored the dynamic characteristics of SSDF attack behavior.…”

Section: Introductionmentioning

confidence: 99%

“…The schemes proposed in [5][6][7] become defective either by the increasing proportion of malicious users or in the face of complex attack strategies. Others in [8][9][10] have no ability to maintain robustness under the presence of a high proportion of MUs, while the rest of the methods, as in [11][12][13], cannot cope with complicated attack patterns.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Zhang

2016

Symmetry

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Abstract:Collaborative spectrum sensing (CSS) was envisioned to improve the reliability of spectrum sensing in centralized cognitive radio networks (CRNs). However, secondary users (SUs)' changeable environment and ease of compromise make CSS vulnerable to security threats, which further mislead the global decision making and degrade the overall performance. A popular attack in CSS is the called spectrum sensing data falsification (SSDF) attack. In the SSDF attack, malicious cognitive users (MUs) send false sensing results to the fusion center, which significantly degrades detection accuracy. In this paper, a comprehensive reputation-based security mechanism against dynamic SSDF attack for CRNs is proposed. In the mechanism, the reliability of SUs in collaborative sensing is measured with comprehensive reputation values in accordance with the SUs' current and historical sensing behaviors. Meanwhile a punishment strategy is presented to revise the reputation, in which a reward factor and a penalty factor are introduced to encourage SUs to engage in positive and honest sensing activities. The whole mechanism focuses on ensuring the correctness of the global decision continuously. Specifically, the proposed security scheme can effectively alleviate the effect of users' malicious behaviors on network decision making, which contributes greatly to enhancing the fairness and robustness of CRNs. Considering that the attack strategy adopted by MUs has been gradually transforming from simplicity, fixedness and singleness into complexity, dynamic and crypticity, we introduce two dynamic behavior patterns (true to false and then to true (TFT) and false to true and then to false (FTF)) to further validate the effectiveness of our proposed defense mechanism. Abundant simulation results verify the rationality and validity of our proposed mechanism.

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Trust management in cognitive radio networks: A survey

2017

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Cognitive radio is proposed to solve the interference and underutilized license problems. With the development of the cognitive functionalities, classical and new security threats become challenging. To make wireless cognitive radio networks more secure and more reliable, several approaches have been proposed in the literature. In recent years, trust and reputation management (TRM) techniques are more and more considered for cognitive radio networks to secure them against the malicious and misbehaving users that try to deprive others from using the white space. The scope of this survey is to give a comprehensive overview about the existing TRM-based mechanisms proposed for the cognitive radio networks. We expose the existing classifications of TRM techniques, then we introduce a new classification that takes into account, more exhaustively, different TRM properties and approaches.Int J Network Mgmt. 2018;28:e1999.wileyonlinelibrary.com/journal/nemThe new classification aspects are the following:1. The network topology: Every network topology may strongly affect the choice of the appropriate TRM mechanism. 2. The detection strategy: At the spectrum detection phase, the SU senses individually or cooperatively the PU activities in the channel. Each detection strategy, used to make the final decision, can be secured by different TRM mechanisms. 3. Trust computation: For each node, the trust value can be computed using intelligent or simple methods (Bayesian network, probability, etc) to distinguish between honest, suspicious, and malicious entities. 4. Spectrum management: To control the spectrum allocation and to manage the channel availability decisions, the CRN may use a centralized, distributed, or clustering spectrum sensing technique. For each spectrum management technique, different TRM approaches are proposed to secure the spectrum detection feature.The remainder of this paper is organized as follows: In section 2, we will review the security attacks and vulnerabilities in CRNs. Then, we will define, in Section 3, the concepts of the TRM. Thus, we will illustrate the existing TRM approaches and classifications. In Section 4, we will present our new classification of TRM approaches in the CRNs. CRNS ATTACKS AND VULNERABILITIESWireless networks are vulnerable to several types of attacks and threats. 2 Indeed, attacks can be classified according to the target layer. Briefly, Table 1 illustrates a list of attacks that may affect all types of wireless networks.Cognitive radio networks are vulnerable to conventional wireless networks attacks 3 but suffers further from specific attacks due to their intrinsic cognitive functionalities. In CRNs, the SUs, competing to exploit the white space, can be honest, selfish, faulty, or malicious. Faulty users may send incorrect sensing outcomes due to defective device, fading location, or shadowing zone. The erroneous spectrum detection affects the final decision and degrades the network performance. On the other hand, the selfish and malicious users aim at monopolizing the f...

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A sensing and etiquette reputation-based trust management for centralized cognitive radio networks

Cited by 29 publications

References 30 publications

Dynamic Trust Threshold Value Based on Market Theory in Cognitive Radio Networks

Dynamic Trust Threshold Value Based on Market Theory in Cognitive Radio Networks

Comprehensive Reputation-Based Security Mechanism against Dynamic SSDF Attack in Cognitive Radio Networks

Trust management in cognitive radio networks: A survey

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