Trust and reputation scheme for clustering in cognitive radio networks

Koh, Chungha; Yau, K. L.A.

doi:10.1049/cp.2014.1407

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…In this context, the authors in one study are interested in securing the cluster from the harmful attacks by proposing a reinforcement learning–based trust and a reputation management approach to determine the credibility of a specific node as time goes by, and to select a honest node as CH in the cluster topology. In fact, due to the reinforcement learning, each SU can keep track and can learn about the neighbor behavior.…”

Section: Trm: Definition and Conceptsmentioning

confidence: 99%

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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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Section: Trm: Definition and Conceptsmentioning

confidence: 99%

“…Therefore, using trust management as a security mechanism for this architecture seems to be very efficient. Several approaches are proposed in literature such as the trust mechanism proposed in Koh et al to secure the cluster‐based architecture.…”

Section: Trm Techniques Classificationmentioning

confidence: 99%

Trust management in cognitive radio networks: A survey

2017

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Reinforcement Learning's Contribution to the Cyber Security of Distributed Systems

Feltus

2022

Research Anthology on Convergence of Blockchain, Internet of Things, and Security

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Reinforcement learning (RL) is a machine learning paradigm, like supervised or unsupervised learning, which learns the best actions an agent needs to perform to maximize its rewards in a particular environment. Research into RL has been proven to have made a real contribution to the protection of cyberphysical distributed systems. In this paper, the authors propose an analytic framework constituted of five security fields and eight industrial areas. This framework allows structuring a systematic review of the research in artificial intelligence that contributes to cybersecurity. In this contribution, the framework is used to analyse the trends and future fields of interest for the RL-based research in information system security.

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Learning Algorithm Recommendation Framework for IS and CPS Security

Feltus

2022

International Journal of Systems and Software Security and Protection

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Artificial intelligence and machine learning have recently made outstanding contributions to the performance of information system and cyber--physical system security. There has been a plethora of research in this area, resulting in an outburst of publications over the past two years. Choosing the right algorithm to solve a complex security problem in a very precise industrial context is a challenging task. Therefore, in this paper, we propose a Learning Algorithm Recommendation Framework that, for a clearly defined situation, guides the selection of learning algorithm and scientific discipline (e.g. RNN, GAN, RL, CNN,...) which have sparked great interest to the scientific community and which therefore offers preponderant elements and benefits for further deployments. This framework has the advantage of having been generated from an extensive analysis of the literature, as illustrated by this paper for the recurrent neural networks and their variations.

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Trust and reputation scheme for clustering in cognitive radio networks

Cited by 3 publications

References 8 publications

Trust management in cognitive radio networks: A survey

Trust management in cognitive radio networks: A survey

Reinforcement Learning's Contribution to the Cyber Security of Distributed Systems

Learning Algorithm Recommendation Framework for IS and CPS Security

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