Supporting secure spectrum sensing data transmission against SSDH attack in cognitive radio ad hoc networks

Feng, Jingyu; Lu, Guangyue; Wang, Honggang; Wang, Xuanhong

doi:10.1016/j.jnca.2016.06.007

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…Maintain the matrix; (5) else set the corresponding row vector to zero; (6) end if (7) end for (8) Then the current matrix T 1 is derived; (9) Evaluate the crowdsensing effect function of this particle; (10) Obtainthis particle's P id (matrix T 2 ) based on crowdsensing effect function; (11) Obtain (matrix T 3 ) with the optimal ; (12) Merge matrix T 1 , T 2 , T 3 ; (13) Optimize the column vectors of the merging matrix; (14) Optimize row vectors of the merging matrix with specific probability using (9); (15) Evaluate the crowdsensing effect function of merging matrix according to (6); ( on (1). This function reflects the sensing effect and its value is between 0 and 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Zhai

Wang²

2017

Wireless Communications and Mobile Computing

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Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

“…According to the sensing results, they estimate the spectrum states cooperatively [5]. In [6], authors propose a two-level defense scheme to solve the attackers in cooperative spectrum sensing. In [7], cooperative spectrum sensing based on crowdsourcing is studied to address the security issue brought by malicious mobile users.…”

Section: Related Workmentioning

confidence: 99%

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Zhai

Wang²

2017

Wireless Communications and Mobile Computing

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“…Cognitive radio network is vulnerable to attacks [12] in which primary user emulation attack is one of the major security threats. Along with security, an iterative resource allocation process is used in the literature [13] to improve cognitive radio network performance through energy management.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimized Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

Rangaraj¹,

Jothiraj²,

Sridevi³

2021

Wireless Pers Commun

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Spectrum utilization is an important factor in Cognitive radio networks, which is accomplished by analyzing the unused spectrum bands of primary users (PU). The secondary users are allowed to access the resources, when the spectral bands are vacant by sensing the spectrum status and thus it reduces the spectrum scarcity among the users. Researchers have paid more attention towards spectrum sensing along with its security factors in cognitive radio networks. In this process, cooperative spectrum sensing is widely adopted in cognitive radio networks due to its robustness. However, the security concerns in cooperative spectrum sensing against attacks must be addressed. The performance of cooperative spectrum sensing will get affected if the fusion center gets wrong information from malicious user. This leads to wrong decision in the fusion center and results into false observations and affects the decision process. In order to overcome these challenges, this research work proposes a hybrid nature inspired and optimized cooperative spectrum sensing against attacks in cognitive radio networks. The proposed model allows the fusion center to remove the uncharacteristic data in the fusion process, which results from the malicious users. The performance analysis of spectrum sensing process under different attacks are analyzed through simulation and later it is compared against conventional methods such as genetic algorithm, particle swarm optimization and differential evolution schemes to validate the improved performance.

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“…In the wideband wireless system, the users exchange compression results (CS -compressive sensing). In [6] the authors assume the two-level protection scheme against interference in the cooperated spectrum analysis. In [7] it is considered the mutual spectrum analysis based on multiple detection with the aim of increasing the protection ratio level.…”

Section: Introductionmentioning

confidence: 99%

Mobile Users’ Multiple Detection Method on the Basis of the Particle Swarm Optimization in the Cognitive Radio Network

Obikhod¹

2018

A.I.S.

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In the article, the object is mobile users' multiple detection processes based on the particle swarm optimization in the cognitive radio network. The aim of the research lies in the field of the algorithms of the mobile users' detection of the cognitive radio system. In addition, it lies in the area of development of the generalized algorithm PSO-NN and improvement the method of multiple detection by using the particle swarm method and convolutional neural network and its realization. The tasks are to develop the multiple detection architecture, the generalized algorithm PSO-NN, to realize the multiple detection algorithm and to model PSO-NN as the algorithm of the multiple detection effectiveness for 50 mobile devices. The methods used are mathematical models based on the principles of the organization and operations of biological neural networks, mathematical learn models, and NP-hard algorithm theory methods. The following results were received. The multiple detection architecture was developed, which differs from the known ones since each location is divided into the subzones. In the different subzones, a mobile user can receive different measuring results in the same channel. Such a division can be used for more flexible data using. Developed generalized algorithm PSO-NN differs from the known ones as it is configured more correctly for the real conditions that inherent in the architecture of the cognitive systems. This algorithm uses particle swarm optimization controlled by the convolutional neural network. Therefore, strict access to spectrum analysis based on mobile users' energy component is provided. Due to the use of the micro particle architecture and convolutional neural networks, detection effectiveness function and global particle location are detected in a more accurate way. Further implementation of the multiple detection algorithm differs from the known ones since after uniting the detected channel users' matrices, only one user is assigned to a specific channel. Such an implementation assumes more realistic search area and speed of the users' detection with found channels. Modeling PSO-NN as the multiple detection effectiveness algorithm for 50 mobile users has several convolutional layers that were generalised with each other. Such architecture can be a confirmation of the fact that the neural network chosen in a practical way completely satisfies the tasks. The modeling result showed that at 20 locations the detection effectiveness with using the algorithm PSO-NN increased by 10% in 20 locations, by 20% in 25 locations, by 20% in 30 locations, by 20% in 35 locations, while the results did not change in 40 locations. K e ywor d s : cognitive radio; particle swarm algorithm; convolutional neural network; radio frequency resource; NP-hard class problem.

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Supporting secure spectrum sensing data transmission against SSDH attack in cognitive radio ad hoc networks

Cited by 8 publications

References 17 publications

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

Hybrid Optimized Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

Mobile Users’ Multiple Detection Method on the Basis of the Particle Swarm Optimization in the Cognitive Radio Network

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