A trust game model for the cognitive radio networks

Bennaceur, Jihen; Idoudi, Hanen; Saı̈dane, Leı̈la Azouz

doi:10.1109/pemwn.2016.7842903

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…In cognitive radio networks, Bennaceur et al proposed another trust game model to take into consideration the penalty of the malicious users. By introducing the trust game model of cooperative sensing spectrum, the secondary users are encouraged to choose the honest strategy by sending correct sensing outcomes [48].…”

Section: Usermentioning

confidence: 99%

Game‐Based Trust in Complex Networks: Past, Present, and Future

Fang

Zhang

et al. 2021

Complexity

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As an efficient approach, the trust policy is implemented to defend against insider attacks in complex networks. However, the imperfection of trust relationships directly hinders the effort to quantitatively calculate trust value, especially in choosing a cooperative partner. Fortunately, the game theory is gradually concerned with addressing the above issue to further enhance security. In this paper, the game theory and the trust policy are reviewed briefly. Then, the research roadmap on game-based trust in complex networks is discussed and analysed deeply. Furthermore, some research directions in the near future are given. It is worth mentioning that our contributions not only describe the evolution of the game-based trust clearly but also suggest the trust mechanism based on the bounded rational game more suitable to uncertain information.

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Section: Usermentioning

confidence: 99%

Game‐Based Trust in Complex Networks: Past, Present, and Future

Fang

Zhang

et al. 2021

Complexity

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“…No profiteering technique is employed and selfish nodes cannot be identified. A trust-based on a noncooperative game is given by Bennaeur et al [2], wherein the penalty associated with the faulty SUs is addressed by exempting the MUs from participating in the sensing process for a particular period of time. However, penalisation of genuine SUs who report falsely owing to channel conditions would face considerable expenses.…”

Section: Related Workmentioning

confidence: 99%

“…The alteration of the sensing report by malicious users (MU) is the spectrum data falsification attack (SSDF) that typically occurs at the data link layer. The SSDF attack can be classified into five classes [2]: (a) always yes: in the absence or presence of the PU, the malicious user always reports the presence of a PU to divest the genuine SU;(b) always no: to induce interference, the malicious user always reports the absence of the PU even in its presence; (c) alternator: the malicious user always conflicts the genuine SUs detection output; (d) selfish: at times, the malicious attacker does not participate in the sensing process to save energy; and (e) contingent: report data falsely in a random manner. They play smartly such that identification becomes difficult.…”

Section: Introductionmentioning

confidence: 99%

An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

Tephillah

Manickam

2020

Wireless Communications and Mobile Computing

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Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

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