A Credibility-Based Defense SSDF Attacks Scheme for the Expulsion of Malicious Users in Cognitive Radio

Du, Hong Zhen; Fu, Shuang; Chu, Hongna

doi:10.14257/ijhit.2015.8.9.25

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…Sequential detection [9][10][11][12][13][14][15][16][17][18][19] Consensus algorithm [20] Hamming distance [21] Dempster-Shafer evidence theory [22,25] Double adaptive threshold approach [23,24] Soft fusion [26] Beta function [27] Security and privacy [28,29] Green communication [30][31][32][33] Secure spectrum and energy efficiency [34] Game theory [35] Mobility management [36,37] Meanwhile, the reputation mechanism is also widely used to secure the CSS. Mapunya and Velempini propose a method to solve Byzantine attack by isolating the forged report data in Reference [20].…”

Section: Methods and Research Directions Literaturementioning

confidence: 99%

Sequential spectrum sensing against random Byzantine attack in cognitive radio networks

Wu,

He,

Gan

et al. 2023

Trans Emerging Tel Tech

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Cognitive radio (CR) technology allows secondary users (SUs) to opportunistically access the licensed spectrum being authorized by the primary users (PUs) and then improves the spectrum utilization. Cooperative spectrum sensing (CSS) is an important component of cognitive radio (CR) technology to detect the PU signal via spatial diversity. However, due to the open facet of CR networks (CRNs), the CSS process may suffer from Byzantine attack where malicious users (MUs) falsify the status information about the PU signal and then cause harmful interference to the PU's communication or selfishly occupy spectrum resources. Otherwise, the cooperative paradigm also results in that multiple SUs need more time to detect the PU signal. The performance and efficiency of CSS will be further degraded due to these two reasons. For this aim, we propose sequential spectrum sensing (SSS) scheme to mitigate the negative impact of Byzantine attack and improve the cooperative efficiency. To this end, encouraged by in‐depth analysis of the blind problem, we formulate a random Byzantine attack model to follow the malicious behaviors from the perspective of MUs. In order to solve the blind problem in the process of the sensing sample fusion, a delivery evaluation mechanism is developed to lay solid foundation for SSS. On this basis, we further evaluate the reputation value (RV) within a sliding window and in order to improve the cooperative efficiency in the reporting stage, we dynamically select the sensing results of the more reliable SUs as the global decision to make a tradeoff between cooperative performance and cooperative efficiency. At last, extensive simulation results show that regardless of the malicious ratio, our proposed SSS only requires 6 samples to provide with 100% detection accuracy in the presence of always attack while also the remarkable performance in the presence of random attack when the malicious ratio exceeds 50%.

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Section: Methods and Research Directions Literaturementioning

confidence: 99%

Sequential spectrum sensing against random Byzantine attack in cognitive radio networks

Wu,

He,

Gan

et al. 2023

Trans Emerging Tel Tech

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“…Due to the importance of cooperation between the entities in CRN, many trust mechanisms such as Du et al and Premarathnea et al are proposed in literature to secure the collaborative exchange.…”

Section: Trm Techniques Classificationmentioning

confidence: 99%

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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“…The proposed algorithm considerably reduces error rate with compare to existing collaborative sensing schemes and improves the spectrum utilization. The defense scheme proposed by Hong Du, Shuang Fu and Hongna Chu [7] use the credit value with each CR user and this value is updated if sensing result is consistence with final decision. In this approach, if reputation value is greater than minimum credit threshold then CR user can take part in next round of sensing.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Estimation of CSS Cognitive Radio Network Performance under Spectrum Sensing Data Falsification Attack

Kadu¹,

Karde²,

Thakare³

2017

IJAERS

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A Credibility-Based Defense SSDF Attacks Scheme for the Expulsion of Malicious Users in Cognitive Radio

Cited by 17 publications

References 12 publications

Sequential spectrum sensing against random Byzantine attack in cognitive radio networks

Sequential spectrum sensing against random Byzantine attack in cognitive radio networks

Trust management in cognitive radio networks: A survey

An Empirical Estimation of CSS Cognitive Radio Network Performance under Spectrum Sensing Data Falsification Attack

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