Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

Khan, Muhammad Sajjad; Khan, Liaqat Ali; Gul, Noor; Amir, Muhammad; Kim, Junsu; Kim, Su Min

doi:10.1155/2020/8846948

Cited by 24 publications

(10 citation statements)

References 23 publications

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“…This is achieved through numerical simulations via Matlab. Monte-Carlo simulations were carried out using the simulation parameters listed in Table II below which are based on the rationale of the other researchers [26], [27], [31]. The performance of the proposed scheme based on the ML algorithm is compared with similar schemes, such as SVM based classification of MUs in CRNs [27], the detection of malicious PU emulation based on a SVM for a mobile CRN using software-defined radio [26], and CSS algorithm based on SVM against spectrum sensing-data-falsification (SSDF) attack [31].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Machine Learning-Based Malicious User Detection in Energy Harvested Cognitive Radio-Internet of Things

Miah¹,

Hossain²,

Ahmed³

et al. 2021

Preprint

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Machine Learning-Based Malicious User Detection in Energy Harvested Cognitive Radio-Internet of Things

Miah¹,

Hossain²,

Ahmed³

et al. 2021

Preprint

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show abstract

“…where δ is an arbitrary constant, w is the weight vector, and b is a threshold value. The margin (overlap region) in the training phase can be obtained as follows [18 ]:…”

Section: Support Vector Machine Regression (Svm)mentioning

confidence: 99%

Performance Analysis of Cooperative Spectrum Sensing using Regression Algorithms

reddy

Meena

2023

Preprint

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Recently, cooperative spectrum sensing (CSS) became an emerging technology due to its advantages in using the spectrum efficiently. In this paper CSS is integrated with the Machine Learning (ML) algorithms to improve the sensing of users which is possible with ML algorithms only in estimating the channel status. Further, the various popular machine learning models for regression are discussed with relevant mathematical analysis such as Linear Regression, Nonlinear Regression, Gaussian Process Regression (GPR), Support Vector Machine Regression (SVM), Generalized Linear Model, Regression Tree, Shallow Neural Network, Deep Neural Network and Regression Tree Ensembles. In this paper, 20 primary users are assumed with different energy levels by maintaining a maximum energy of 20 units and a threshold value of 4 units is fixed for all four scenarios and obtained results are analyzed. In this regard, we concluded a best fit line to extract the features such that the linear regression has been chosen. In addition, various transformations also applied for further improvement in regression to obtain the best results.

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“…In this subsection, we compare the results obtained by the proposed with prior research works. We have compared the proposed work with prior works SVM [46], ML-Neural Network (ML-NN) [47], 2D-Beamforming [48], OLSR [49], and 5G-VANET [50]. The detailed comparison among existing works is summarized in Table 4.…”

Section: Comparative Analysismentioning

confidence: 99%

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

Pari

Jaisankar

2022

Symmetry

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The Internet of Vehicles (IoV) is witnessed to play the leading role in the future of Intelligent Transportation Systems (ITS). Though many works have focused on IoV improvement, there is still a lack of performance due to insufficient spectrum availability, lower data rates, and the involvement of attackers. This paper considers all three issues by developing a novel mmWave-assisted Cognitive Radio based IoV (CR-IoV) model. The integration of CR in IoV resolves the issue of spectrum management, while mmWave technology ensures symmetry in acquiring higher data rates for Secondary Users (SUs). With the proposed mmWave-assisted CR-IoV model, symmetric improvements in network performance were achieved in three main areas: security, beamforming, and routing. Optimum detection mechanisms isolate malicious Secondary Users (SUs) in the overall network. First, Spectrum Sensing Data Falsification (SSDF) attack is detected by a Hybrid Kernel-based Support Vector Machine (HK-SVM), which is the lightweight Machine Learning (ML) technique. Then, the Primary User Emulation (PUE) attack is detected by a hybrid approach, namely the Fang Algorithm-based Time Difference of Arrival (FA-TDoA) method. Further, security is assured by validating the legitimacy of each SU through a Lightweight ID-based Certificate Validation mechanism. To accomplish this, we employed the Four Q-curve asymmetric cryptographic algorithm. Overall, the proposed dual-step security provisioning approach assures that the network is free from attackers. Next, beamforming is performed for legitimate SUs by a 3D-Beamforming algorithm that relies on Array Factor (AF) and Beampattern Function. Finally, routing is enabled by formulating Forwarding Zone (FZ) based on the forwarding angle. In the forwarding zone, optimal forwarders are selected by the Multi-Objective Whale Optimization (MOWO) algorithm. Here, a new potential score is formulated for fitness evaluation. Finally, the proposed mmWave-assisted CR-IoV model is validated through extensive simulations in the ns-3.26 simulation tool. The evaluation shows better performance in terms of throughput, packet delivery ratio, delay, bit error rate, and detection accuracy.

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Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

Cited by 24 publications

References 23 publications

Machine Learning-Based Malicious User Detection in Energy Harvested Cognitive Radio-Internet of Things

Machine Learning-Based Malicious User Detection in Energy Harvested Cognitive Radio-Internet of Things

Performance Analysis of Cooperative Spectrum Sensing using Regression Algorithms

Defense against SSDF Attack and PUE Attack in CR-Internet of Vehicles (IoVs) for Millimeter Wave Massive MIMO Beamforming Systems

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