Dynamic cooperator selection in cognitive radio networks

Vučević, Nemanja; Akyildiz, Ian F.; Pérez‐Romero, Jordi

doi:10.1016/j.adhoc.2011.01.020

Cited by 8 publications

(14 citation statements)

References 17 publications

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“…Vučević et al [38] propose a RL model with suitability value for TRM S(1) applied to channel sensing P(1) in order to identify honest nodes for collaboration in CRNs. In this model, each node selects its neighbor nodes to collaborate by evaluating their suitability values, which are derived from the outcome of the final decisions.…”

Section: Rl Model With Suitability Valuementioning

confidence: 99%

“…A.2 Unintentional attack. In this attack, non-malicious nodes unintentionally launch attacks (e.g., generating inaccurate sensing outcomes and making incorrect decisions) due to manipulation from malicious nodes, technological limitation (e.g., hardware and software errors), or due to environmental factors (e.g., sensors located in shadowing zone) [38]. As these attacks are unintentional, some measures need to be incorporated in the detection mechanism to ensure that these non-malicious nodes are not misdetected as malicious [45].…”

Section: Types Of Attacksmentioning

confidence: 99%

“…CR uses channel sensing technique, such as energy detection, to sense for white spaces. Generally speaking, exploration enables SUs to detect the white spaces at the earliest possible time, while exploitation enables SUs to efficiently access and utilize the channel(s) [38]. P.2 Channel access.…”

Section: Types Of Wireless Network and Application Schemesmentioning

confidence: 99%

“…Nodes may change from honest to malicious as time progresses, and vice versa [38]. RL has been applied to provide a dynamic and intelligent mechanism to monitor the nodes' behavior at all times.…”

Section: Challengesmentioning

confidence: 99%

“…RL has been applied in TRM to identify malicious nodes and address the challenge of dynamicity in the malicious nodes' behavior C(1) [38]. S.2 Anti-jamming.…”

Section: Existing Schemesmentioning

confidence: 99%

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Application of reinforcement learning for security enhancement in cognitive radio networks

Ling

Yau

Qadir

et al. 2015

Applied Soft Computing

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Please cite this article in press as: M.H. Ling, et al., Application of reinforcement learning for security enhancement in cognitive radio networks, Appl. Soft Comput. J. (2015), http://dx. a b s t r a c tCognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to sense for and opportunistically operate in underutilized licensed channels, which are owned by the licensed users (or primary users, PUs). Cognitive radio network (CRN) has been regarded as the next-generation wireless network centered on the application of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and dynamically reconfigure its operating parameters, including the sensing and transmission channels, for network performance enhancement. This motivates the use of artificial intelligence to enhance security schemes for CRNs. Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration. In addition these techniques cannot prevent an authenticated node from acting maliciously. In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or near-optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs. RL, which is an artificial intelligence technique, has the ability to learn new attacks and to detect previously learned ones. RL has been perceived as a promising approach to enhance the overall security aspect of CRNs. RL, which has been applied to address the dynamic aspect of security schemes in other wireless networks, such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs. We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge, this is the first survey article that focuses on the use of RL-based techniques for security enhancement in CRNs.

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Section: Rl Model With Suitability Valuementioning

confidence: 99%

Section: Types Of Attacksmentioning

confidence: 99%

Section: Types Of Wireless Network and Application Schemesmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

“…RL has been applied in TRM to identify malicious nodes and address the challenge of dynamicity in the malicious nodes' behavior C(1) [38]. S.2 Anti-jamming.…”

Section: Existing Schemesmentioning

confidence: 99%

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Application of reinforcement learning for security enhancement in cognitive radio networks

Ling

Yau

Qadir

et al. 2015

Applied Soft Computing

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Editorial for special issue on “Cognitive radio ad hoc networks”

2012

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A Security-Enhanced Cluster Size Adjustment Scheme for Cognitive Radio Networks

et al. 2019

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Cognitive radio network (CRN) is the next generation wireless network that allows unlicensed users [secondary users (SUs)] to explore and use the underutilised licensed channels (white spaces) owned by licensed users (primary users). The purpose is to increase the spectrum utilization for enhanced network performance. Clustering segregates SUs in a CRN into logical groups (clusters) with each consisting of a leader (cluster head) and member nodes. A budget-based cluster size adjustment scheme is applied to enable each cluster to adjust its number of member nodes in its cluster based on the availability of white spaces in order to improve network scalability. However, cluster size adjustment is prone to attacks by malicious SUs that launch random and intelligent attacks. Hence, we incorporate an artificial intelligence approach called reinforcement learning (RL) into a trust model to countermeasure the random and intelligent attacks. The simulation results show that RL-based trust model increases the utilization of white spaces and cluster size to improve network scalability and enhance network performance despite the presence of RL-based intelligent attacks. INDEX TERMS Artificial intelligence, reinforcement learning, attacks, trust model, cognitive radio.

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Dynamic cooperator selection in cognitive radio networks

Cited by 8 publications

References 17 publications

Application of reinforcement learning for security enhancement in cognitive radio networks

Application of reinforcement learning for security enhancement in cognitive radio networks

Editorial for special issue on “Cognitive radio ad hoc networks”

A Security-Enhanced Cluster Size Adjustment Scheme for Cognitive Radio Networks

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