Intelligent resource allocation scheme for the cognitive radio network in the presence of primary user emulation attack

Das, Deepa; Das, Susmita

doi:10.1049/iet-com.2016.0235

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…In [12], attackers launch jamming attacks on the control channel of multi-channel ad hoc networks, and so the nodes in the clusters adopt a randomized and distributed scheme based on frequency hopping to establish a new control channel. In [13], a resource allocation scheme is used to provide energy efficiency by selecting legitimate SUs in CRNs in the presence of PUE attacks.…”

Section: B Related Workmentioning

confidence: 99%

A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

Ling

Yau

Qadir

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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Cognitive radio enables secondary users (SUs) to explore and exploit the underutilized licensed channels (or white spaces) owned by the primary users. To improve the network scalability, the SUs are organized into clusters. This article proposes a novel artificial intelligence based trust model approach that uses reinforcement learning (RL) to improve traditional budget-based cluster size adjustment schemes. The RL-based trust model enables the clusterhead to observe and learn about the behaviors of its SU member nodes, and revoke the membership of malicious SUs in order to ameliorate the effects of intelligent and collaborative attacks, while adjusting the cluster size dynamically according to the availability of white spaces. The malicious SUs launch attacks on clusterheads causing the cluster size to become inappropriately sized while learning to remain undetected. In any attack and defense scenario, both the attackers and the clusterhead adopt RL approaches. Simulation results have shown that the single-agent RL (SARL) attackers have caused the cluster size to reduce significantly; while the SARL clusterhead has slightly helped increase its cluster size, and this motivates a rule-based approach to efficiently counterattack. Multi-agent RL attacks have shown to be less effective in an operating environment that is dynamic.Index Terms-Cognitive radio ad hoc networks, clustering methods, trust management, artificial intelligence, security.

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Section: B Related Workmentioning

confidence: 99%

A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

Ling

Yau

Qadir

et al. 2019

IEEE Trans. Cogn. Commun. Netw.

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“…An attack is considered strong if it involves a minimal number of adversaries performing minimal operations but causing maximum damage or loss to the PUs and or SUs in the network. A brief summary of these attacks is given in Table 1 [19,[30][31][32][33][34][35][36][37][38][39][40][41][42]. Table 1.…”

Section: Threats On Crnmentioning

confidence: 99%

“…Biased utility attack [33,34] Asynchronous sensing attack [35][36][37][38] Byzantine attack [39] False feedback attack [33] Selfish misbehaviour attack [39,40] SU tweaks parameters of the utility function Network Endo-parasite attack [33,34] Channel Ecto-parasite attack [33,34] Low cost ripple effect attack [33,34] SU attempts to increase the interference Key depletion attack [33,34] High round trip times and frequent retransmissions increase key usage Any attacks against confidentiality, integrity, authentication, non-repudiation, access control, and availability [19,33] Any attacks on below layers from application layer impact adversely on the layer Jellyfish attack [41] Routing information jamming attack [42] Performed in a network layer but affected to the other layers…”

Section: Attacks Propertiesmentioning

confidence: 99%

Delegation Based User Authentication Framework over Cognitive Radio Networks

Kim

Ryu

2017

JSAN

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To address the ever increasing demand for wireless bandwidth, cognitive radio networks (CRNs) have been proposed to improve the efficiency of channel utilization. CRN permits unlicensed users to utilize the idle spectrum as long as it does not introduce interference to the primary users due to the Federal Communications Commission's recent regulatory policies. In this paper, we first identify some required distinctive security and privacy features for CRNs focused on ECMA-392, which is the first industrial standard for personal or portable devices in the television white spaces. After that, we propose a delegation based user authentication framework as a basic security and privacy module with full consideration of the required features over CRNs. The proposed framework provides privacy preserving yet accountable security within the CRN entities. Security and privacy analyses show that the proposed framework supports unlinkability, context privacy, anonymity, no registration and conditional traceability, which are the required security and privacy aspects in CRNs.

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“…It also took the maximization of the SU's rate as the design object. Furthermore, the works in [16], [17] investigated the energy efficiency (EE) maximization problem by SU selection, power allocation and sensing time assignment in the presence of PUEA. However, the above works did not consider the delay QoS requirement of the SU's practical traffic.…”

Section: Introductionmentioning

confidence: 99%

Multiple-Phase Energy Detection and Effective Capacity Based Resource Allocation Against Primary User Emulation Attacks in Cognitive Radio Networks

2020

KSII TIIS

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Cognitive radio (CR) is regarded as an effective approach to avoid the inefficient use of spectrum. However, CRNs have more special security problems compared with the traditional wireless communication systems due to its open and dynamic characteristics. Primary user emulation attack (PUEA) is a common method which can hinder secondary users (SUs) from accessing the spectrum by transmitting signals who has the similar characteristics of the primary users' (PUs) signals, and then the SUs' quality of service (QoS) cannot be guaranteed. To handle this issue, we first design a multiple-phase energy detection scheme based on the cooperation of multiple SUs to detect the PUEA more precisely. Second, a joint SUs scheduling and power allocation scheme is proposed to maximize the weighted effective capacity of multiple SUs with a constraint of the average interference to the PU. The simulation results show that the proposed method can effectively improve the effective capacity of the secondary users compared with the traditional overlay scheme which cannot be aware of the existence of PUEA. Also the good delay QoS guarantee for the secondary users is provided.

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Intelligent resource allocation scheme for the cognitive radio network in the presence of primary user emulation attack

Cited by 8 publications

References 24 publications

A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

A Reinforcement Learning-Based Trust Model for Cluster Size Adjustment Scheme in Distributed Cognitive Radio Networks

Delegation Based User Authentication Framework over Cognitive Radio Networks

Multiple-Phase Energy Detection and Effective Capacity Based Resource Allocation Against Primary User Emulation Attacks in Cognitive Radio Networks

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