A Trust Management Model in Centralized Cognitive Radio Networks

Chen²,

2015 IEEE 16th International Conference on Communication Technology (ICCT)

et al. 2015

There are two challenges when performing cooperative spectrum sensing considering security issues in cognitive radio networks. One is to distinguish honest secondary users in the fading area with malicious cognitive users and the other is how to stimulate cognitive users to take part in cooperative spectrum sensing so that the whole system performance could be enhanced. In this paper, we propose a trusted social behavior inspired with clustering scheme (TRUSTER) for cooperative spectrum sensing in cognitive radio networks. Simulations demonstrate that TRUSTER scheme is at least 37.5% times faster than traditional clustering method when isolating malicious SUs and the false alarm probability is lower than the traditional base scheme.

Section: Related Workmentioning

confidence: 99%

TRUSTER: Trusted social behavior inspired scheme for cooperative spectrum sensing

Chen²,

2015 IEEE 16th International Conference on Communication Technology (ICCT)

et al. 2015

International Conference on Frontiers of Communications, Networks and Applications (ICFCNA 2014 - Malaysia)

“…These kinds of nodes may be from one of the following categories [6]: a) Selfish node falsifies sensing outcomes and influences the channel sensing decision so that it can gain access to channels or other benefits. b) Aggressive node falsifies sensing outcomes or breaks the channel access rules which may affect PUs' or SUs' network performance.…”

Section: Trust and Reputation Management In Cognitive Radio Networkmentioning

confidence: 99%

Trust and reputation scheme for clustering in cognitive radio networks

Koh

Yau

2014

Cognitive radio is the next-generation wireless communication network that improves the efficiency of the radio spectrum through exploitation of underutilized licensed spectrum (or white spaces). This paper applies a reinforcement learning-based Trust and Reputation Management (TRM) scheme to cluster-based routing and shows network performance enhancement, including throughput and rewards. Generally speaking, clustering forms logical groups of nodes throughout the entire network, and routing establishes routes on the underlying clustered network which is distributed in nature. Each cluster is comprised of a clusterhead (or the leader of the cluster) and member nodes. TRM is applied as a security measure to allow each node to determine credibility of its neighbouring nodes. Reinforcement Learning (RL) is applied to keep track of the credibility level of a node, and it provides reward based on a node's behaviour; subsequently the reward is applied to select clusterheads. The selection of trusted nodes as clusterheads has been a problem that is of great significance due to the important role played by clusterheads as the local point of process for various applications such as channel sensing and routing. Our simulation results show that the RL-based TRM approach applied to clusterhead selection helps to reduce the effects of attacks from malicious nodes, and this has been shown to increase average throughput and reward rate, as well as to reduce changes of clusterhead in a cluster.

“…Pei et al . propose the trust management model (TMM) for centralized and collaborative spectrum sensing P(1) and channel access P(2) applications in order to detect malicious SUs (including faulty and selfish) SUs and collusion attacks A(2.1), as well as to protect PUs from interference. Consider a centralized SU network comprised of a SU BS and its SU hosts.…”

Section: Trust and Reputation Management Challenges And Attack Modelsmentioning

confidence: 99%

“…For instance, Zhang and Xia and Pei et al . use hard sensing outcomes while Wang et al . and Kaligineedi et al .…”

Section: Open Issuesmentioning

confidence: 99%

Trust and reputation management in cognitive radio networks: a survey

Ling

Yau

Poh

2013

Security Comm Networks

Cognitive radio (CR), which is the next generation wireless communication system, enables unlicensed users or secondary users (SUs) to exploit underutilized spectrum (called white spaces) owned by the licensed users or primary users (PUs) so that bandwidth availability improves at the SUs, which helps to improve overall spectrum utilization. Collaboration is an intrinsic characteristic of CR to improve network performance. For instance, in collaborative spectrum sensing, SU hosts generate sensing outcomes, and collaborate amongst themselves through making final decisions at a decision fusion center in order to improve the accuracy of spectrum sensing. The requirement to collaborate has inevitably opened doors to various forms of attacks by malicious SUs, and this critical issue can be addressed using trust and reputation management (TRM), and so this is the focus of this article. Generally speaking, TRM detects malicious SUs, including honest SUs that turn malicious. Hence, TRM is of paramount importance in most kinds of schemes that require collaboration in CR networks. Our contribution in this article is as follows. This article provides an extensive survey on the application of TRM in various schemes in CR networks in order to ameliorate the effects of malicious SUs in collaboration. The discussion is presented with respect to a TRM taxonomy, various approaches to achieve TRM, various attack models, as well as the challenges and characteristics associated with TRM. Because of the significance of TRM in collaboration, this article presents a wide range of open issues to warrant further research in this area. Copyright © 2013 John Wiley & Sons, Ltd.