A Multi-factor Trust Management Scheme for Secure Spectrum Sensing in Cognitive Radio Networks

Kar, Sumit; Sethi, Srinivas; Sahoo, Ramesh K.

doi:10.1007/s11277-017-4621-5

Cited by 26 publications

(16 citation statements)

References 27 publications

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“…Authors in [18], [19], proposed a mathematical model based on trust and reputation factor. Kar et al in [20], used four parameters in their work in order to calculate the trustworthiness (sensing reputation). These factors were active factor, consistency factor, incentive factor and trust factor.…”

Section: Related Workmentioning

confidence: 99%

Trust Aware Scheme based Malicious Nodes Detection under Cooperative Spectrum Sensing for Cognitive Radio Networks

Kumar¹,

Gupta²,

Tapwal³

2020

Preprint

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<div>Emerging of Cognitive Radio (CR) technology has</div><div>provided optimistic solution for the dearth of spectrum by</div><div>improving the spectrum utilization. The opportunistic use of the spectrum is enabled by spectrum sensing which is one of the key functionality of CR systems. To perform the interference free transmission in a cognitive radio networks, an important part for unlicensed user is to identify a licensed user with the help of spectrum sensing. Recently, the Cooperative Spectrum Sensing has been widely used in the literature where various scattered unlicensed users collaborate with each other to make the final sensing decision. This overcome the hidden terminal problem</div><div>and also improve the overall reliability of the decisions made</div><div>about the presence or absence of a licensed user. Each unlicensed user send the sensing results to the base station for final decision. However there exist some nodes which do not provide the correct sensing results to maximize their own profit which can highly degrade the CR network functionality. In this paper, a trust aware model is proposed for detection of misbehaving nodes such that their sensing reports can be filter out from the final result. The performance evaluation of the proposed scheme is done by checking its robustness and efficiency against various possible attacks. </div>

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Section: Related Workmentioning

confidence: 99%

Trust Aware Scheme based Malicious Nodes Detection under Cooperative Spectrum Sensing for Cognitive Radio Networks

Kumar¹,

Gupta²,

Tapwal³

2020

Preprint

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“…In [7], the authors considered the construction of trust mechanism from the perspective of the access competition related to vacant PU spectrum bands. In [8], the authors proposed a trust management scheme by considering multiple decision factors (hereinafter "MFTM"), including (a) history-based trust factor, the trust level of an SU during the period of spectrum sensing, (b) active factor, the level of activity of an SU in the process of spectrum sensing, (c) incentive factor, a reward or incentive for the honest SUs, also serving as a punishment with decrease in trust level for the attackers, and (d) consistency factor, the constancy of maintaining a good trust level. The commonality of these existing typical trust schemes is that the trust value of an SU can be calculated by his previous sensing behaviors and the sensing data of malicious SUs should be deleted when making a final decision.…”

Section: Trust Mechanismmentioning

confidence: 99%

“…That is, such two kinds of SSDF attack patterns are launched in the static and individual way. This original SSDF attack patterns [4] can be easily detected by current trust mechanism such as [5][6][7][8], since the original SSDF attackers will get a lower trust value when they always submit false sensing data individually.…”

Section: Dc-ssdf Attack Overviewmentioning

confidence: 99%

“…deleted 's sensing data (6) else (7) if == then (8) ℎ = ℎ −1 + 1 (9) else (10) = −1 + 1 (11) end if (12) end if (13) end for Procedure 4: Perfect trust mechanism.…”

Section: Perfect Trust Mechanismmentioning

confidence: 99%

“…Such attackers will hold a lower trust value when they always submit false sensing data individually. Various trust mechanism studies have been proposed [5][6][7][8]. They can estimate whether cooperating SUs are honest or not by their sensing behaviors in the past and then give low weights to malicious SUs or even delete their sensing data when making a final decision.…”

Section: Introductionmentioning

confidence: 99%

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Securing Cooperative Spectrum Sensing against DC-SSDF Attack Using Trust Fluctuation Clustering Analysis in Cognitive Radio Networks

Zhao

Feng

2019

Wireless Communications and Mobile Computing

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Cooperative spectrum sensing (CSS) has been recognized as a forceful approach to promote the utilization of spectrum bands. Nevertheless, all secondary users (SU) are assumed as honest in CSS, thus giving opportunities for attackers to launch the spectrum sensing data falsification (SSDF) attack. To defend against such attack, many efforts have been made to trust mechanism. In this paper, we argue that securing CSS with only trust mechanism is not enough and report the description of dynamic-collusive SSDF attack (DC-SSDF attack). To escape the detection of trust mechanism, DC-SSDF attackers can maintain high trust by submitting true sensing data dynamically and then fake sensing data in the collaborative manner to increase their attack strength. Noting that the resonance phenomenon may appear in the trust value curve of DC-SSDF attackers, a defense scheme called TFCA is proposed from the design idea of trust fluctuation clustering analysis to suppress DC-SSDF attack. In the TFCA scheme, the decreasing property of trust value in the resonance phenomenon is adopted to measure the similarity distance between two attackers. Based on the similarity distance computation, the binary clustering algorithm is designed by electing initial binary samples to identify DC-SSDF attackers. Finally, trust mechanism can be perfected by TFCA to correct DC-SSDF attackers’ trust value. Simulation results show that our TFCA scheme can improve the accuracy of trust value calculation, thus reducing the strength of DC-SSDF attack successfully.

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Spectrum sensing challenges & their solutions in cognitive radio based vehicular networks

Hossain

Noor

Azzuhri

et al. 2021

Int J Communication

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Summary Vehicular ad hoc network (VANET) has been established to mitigate road collisions and traffic congestion and provide infotainment facilities to users. Allocated channels to VANET, which is the DSRC (Dedicated Short‐Range Communication), are not adequate for the full implementation of VANET. Cognitive radio (CR) can be used to alleviate this issue. CR is a programmable and intelligent radio system capable of reaching various frequency ranges. CR needs to conduct spectrum sensing to get these bands. A vehicle fitted with a CR can sense the licensed spectrum to locate the vacant spectrum (which is not used by any licensed user) when the DSRC is wholly occupied. Compared to other CR networks, VANET faces specific additional difficulties related to spectrum sensing, such as periodic topological shifts due to high‐speed mobility, multipath fading and shadowing issues, and heterogeneous quality of service (QoS) specifications. This paper explains these problems in depth. All the problems and issues were explored from the perspective of the CR focused on VANET. Probable ideas and directions to overcome these problems have also been presented. We have provided a conceptual framework for spectrum sensing. The framework resolves a variety of issues and concerns that are discussed in the paper. It gives much better results than conventional sensing techniques.

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A Multi-factor Trust Management Scheme for Secure Spectrum Sensing in Cognitive Radio Networks

Cited by 26 publications

References 27 publications

Trust Aware Scheme based Malicious Nodes Detection under Cooperative Spectrum Sensing for Cognitive Radio Networks

Trust Aware Scheme based Malicious Nodes Detection under Cooperative Spectrum Sensing for Cognitive Radio Networks

Securing Cooperative Spectrum Sensing against DC-SSDF Attack Using Trust Fluctuation Clustering Analysis in Cognitive Radio Networks

Spectrum sensing challenges & their solutions in cognitive radio based vehicular networks

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