Support vector machine approach of malicious user identification in cognitive radio networks

Arshid, Kaleem; Zhang, Jianbiao; Hussain, Iftikhar; Lema, Gebrehiwet Gebrekrstos; Yaqub, Muhammad; Munir, Rizwan

doi:10.1007/s11276-022-03055-0

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…In [11], a support vector machine (SVM) learning technique is mainly developed to learn performance of malicious consumers and it categorizes genuine as well as mischievous users. A particle swarm optimization (PSO) model too combined to absorb minimum probable differences malicious consumer's energy report deviance from genuine SUs.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Almuqren,

Maray,

Alotaibi

et al. 2024

IEEE Access

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Malicious user recognition for spectrum sensing in Cognitive Radio Networks (CRNs) is a serious safety feature to safeguard effective and trustworthy process of these systems. Spectrum sensing permits CRNs to identify and employ accessible spectrum bands. As well as it is available to prospective interference and mischievous actions. To preserve network integrity, recognition of malicious consumers is vital. Deep learning (DL) based malicious consumer classification powers advanced neural network frameworks to recognize and flag possible threats inside a network. By examining numerous amounts of information, DL techniques can distinguish patterns as well as anomalies that are connected with malicious user performance plus system intrusions, scams or irregular action. This technique provides flexibility benefit that permits a network to learn and develop in evolving threats. It also offers an effectual revenue of improving network security in the difficult and active digital landscape. Therefore, this article develops an Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing (ODL-MUDSS) in the CRN. The main intention of ODL-MUDSS model focused on automated identification and classification of MUs in CRN. To accomplish this, the ODL-MUDSS model primarily applies deep belief network (DBN) methodology for automated and accurate detection of MUs. In addition, recognition performance of DBN technique can be enhanced by use of sand cat swarm optimization (SCSO) algorithm and thereby improves the detection results. The performance validation of ODL-MUDSS technique is observed under different processes. The comprehensive outcomes stated enhanced performance of ODL-MUDSS model over other existing models with maximum accuracy of 97.75%, precision of 97.75%, recall of 97.75%, and F-score of 97.75%.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Almuqren,

Maray,

Alotaibi

et al. 2024

IEEE Access

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“…In reference 20, an ant colony optimization and differential evolution model was used to secure CSS against attack in CRNs. A support vector machine was used to learn the malicious behavior and identify malicious users in references 21,22. Based on a reinforcement learning algorithm, an adaptive trust threshold model was proposed to deal with ordinary and intelligent SSDF attack in reference 23.…”

Section: Introductionmentioning

confidence: 99%

Less sample‐cooperative spectrum sensing against large‐scale Byzantine attack in cognitive wireless sensor networks

Wu,

Dai,

Teng

et al. 2024

Trans Emerging Tel Tech

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Cooperative spectrum sensing (CSS) has emerged as a promising strategy for identifying available spectrum resources by leveraging spatially distributed sensors in cognitive wireless sensor networks (CWSNs). Nevertheless, this open collaborative approach is susceptible to security threats posed by malicious sensors, specifically Byzantine attack, which can significantly undermine CSS accuracy. Moreover, in extensive CWSNs, the CSS process imposes substantial communication overhead on the reporting channel, thereby considerably diminishing cooperative efficiency. To tackle these challenges, this article introduces a refined CSS approach, termed weighted sequential detection (WSD). This method incorporates channel state information to validate the global decision made by the fusion center and assess the trust value of sensors. The trust value based weight is assigned to sensing samples, which are then integrated into a sequential detection framework within a defined time window. This sequential approach prioritizes samples based on descending trust values. Numerical simulation results reveal that the proposed WSD outperforms conventional fusion rules in terms of the error probability, sample size, achievable throughput, and latency, even under varying degrees of Byzantine attack. This innovation signifies a substantial advancement in enhancing the reliability and efficiency of CSS.

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Malicious Users Detection in OFDM-based Cognitive Radio-Internet of Things using Machine Learning: Simulation and Performance

Miah,

Monsanto Suarez,

Armada

2023

2023 European Conference on Communication Systems (ECCS)

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Support vector machine approach of malicious user identification in cognitive radio networks

Cited by 4 publications

References 35 publications

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing in Cognitive Radio Networks

Less sample‐cooperative spectrum sensing against large‐scale Byzantine attack in cognitive wireless sensor networks

Malicious Users Detection in OFDM-based Cognitive Radio-Internet of Things using Machine Learning: Simulation and Performance

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