scholarly journals Mitigation strategy against spectrum-sensing data falsification attack in cognitive radio sensor networks

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987064 ◽  
Author(s):  
Runze Wan ◽  
Lixin Ding ◽  
Naixue Xiong ◽  
Xing Zhou
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Impact Factors ◽  
Fusion Center ◽  
Sensing Data ◽  
Attack Model ◽  
Cognitive Radio Sensor Networks ◽  
Combination Scheme ◽  
Sensing Performance

To detect the primary user’s activity accurately in cognitive radio sensor networks, cooperative spectrum sensing is recommended to improve the sensing performance and the reliability of spectrum-sensing process. However, spectrum-sensing data falsification attack being launched by malicious users may lead to fatal mistake of global decision about spectrum availability at the fusion center. It is a tough task to mitigate the negative effect of spectrum-sensing data falsification attack and even eliminate these attackers from the network. In this article, we first discuss the randomly false attack model and analyze the effects of two classes of attacks, individual and collaborative, on the global sensing performance at the fusion center. Afterwards, a linear weighted combination scheme is designed to eliminate the effects of the attacks on the final sensing decision. By evaluating the received sensing result, each user can be assigned a weight related to impact factors, which includes result consistency degree and data deviation degree. Furthermore, an adaptive reputation evaluation mechanism is introduced to discriminate malicious and honest sensor node. The evaluation is conducted through simulations, and the results reveal the benefits of the proposed in aspect of mitigation of spectrum-sensing data falsification attack.

scholarly journals A Cooperative Spectrum Sensing Scheme Based on Extension Theory for Cognitive Radio Sensor Networks

2014 ◽  
Vol 8 (1) ◽  
pp. 64-70
Author(s):  
Sheng Ouyang ◽  
Pin Wan ◽  
Yonghua Wang ◽  
Liyuan Wang ◽  
Qinruo Wang
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Extension Theory ◽  
Fusion Center ◽  
Final Decision ◽  
Trust Evaluation ◽  
Cognitive Radio Sensor Networks ◽  
Dependent Function

This paper proposes a cooperative censoring spectrum sensing scheme based on dependent function of extension theory for Cognitive Radio Sensor Networks (CRSN). The scheme uses the dependent function of Extension theory to identify the presence or absence of the licensed user's (LU) signal, and then calculate the related degree through dependent function to identify the initial test results of licensed users, and then send these results to the fusion center. Use a trust evaluation scheme based on noise jamming and channel attenuation for each node, and then this trust evaluation result of each node is sent to the fusion center. The fusion center makes the final decision by the K-M rule. Simulation results show that the proposed scheme could improve the detect probability effectively.

Spectrum Sensing in Cognitive Radio Sensor Networks

2014 ◽  
pp. 48-72 ◽  
Author(s):  
Farooq Alam ◽  
Zahooruddin ◽  
Ayaz Ahmad ◽  
Muhammad Iqbal
Keyword(s):  
Wireless Sensor Networks ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Wireless Sensor ◽  
Research Issues ◽  
Comprehensive Review ◽  
Cognitive Radio Sensor Networks ◽  
Cognitive Radio Sensor Network ◽  
Core Issues

In this chapter, the authors provide a comprehensive review of spectrum sensing in cognitive radio sensor networks. Firstly, they focus on general techniques utilized for spectrum sensing in wireless sensor networks. To have good understanding of core issues of spectrum sensing, the authors then give a brief description of cognitive radio networks. Then they give a thorough description of the main techniques that can be helpful in doing spectrum sensing in cognitive radio sensor network. The authors conclude this chapter with open research issues and challenges that need to be addressed to provide efficient spectrum sensing in order to minimize the limitations in cognitive radio sensor networks.

scholarly journals A Linear Weighted Sum Multi-objective Optimization Algorithm Based on PSO for Wideband Spectrum Sensing

2015 ◽  
Vol 11 (9) ◽  
pp. 9 ◽  
Author(s):  
Yonghua Wang ◽  
Yuehong Li ◽  
Yiquan Zheng ◽  
Ting Liang ◽  
Yuli Fu
Keyword(s):  
Particle Swarm Optimization ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cognitive Radio Sensor Networks ◽  
Wideband Spectrum Sensing

In order to maximize throughput and minimize interference of the wideband spectrum sensing problem in OFDM cognitive radio sensor networks, a linear weighted sum multi-objective algorithm based on the Particle Swarm Optimization is proposed. The multi-objective optimization advantages of Particle Swarm Optimization are utilized to solve the optimal threshold vector of the spectrum sensing problem in OFDM cognitive radio sensor networks. So the network can get a larger throughput under the condition of small interference. The simulation results show that the proposed algorithm can make larger throughput while keeping the interference is smaller in OFDM cognitive radio sensor networks. Thus the spectrum resources are used more effectively.

A SSDF-Attack-Exclusion Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

2014 ◽  
Vol 556-562 ◽  
pp. 5219-5222
Author(s):  
Wei Wu ◽  
Xiao Fei Zhang ◽  
Xiao Ming Chen
Keyword(s):  
Cognitive Radio ◽  
Utility Function ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Fusion Center ◽  
Sensing Data ◽  
Single User ◽  
Simulation Results

Compared with the single user spectrum sensing, cooperative spectrum sensing is a promising way to improve the detection precision. However, cooperative spectrum sensing is vulnerable to a variety of attacks, such as the spectrum sensing data falsification attack (SSDF attack). In this paper, we propose a concise cooperative spectrum sensing scheme based on a reliability threshold. We analyze the utility function of SSDF attacker in this scheme, and present the least reliability threshold for the fusion center against SSDF attack. Simulation results show that compared with the traditional cooperative spectrum sensing scheme, the SSDF attacker has a much lower utility in our proposed scheme, which drives it not to attack any more.

scholarly journals Reliable Machine Learning Based Spectrum Sensing in Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Hurmat Ali Shah ◽  
Insoo Koo
Keyword(s):  
Machine Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Posterior Probability ◽  
Nearest Neighbor ◽  
Probability Of Detection ◽  
Fusion Center ◽  
Training Phase ◽  
K Nearest Neighbor ◽  
Combination Scheme

Spectrum sensing is of crucial importance in cognitive radio (CR) networks. In this paper, a reliable spectrum sensing scheme is proposed, which uses K-nearest neighbor, a machine learning algorithm. In the training phase, each CR user produces a sensing report under varying conditions and, based on a global decision, either transmits or stays silent. In the training phase the local decisions of CR users are combined through a majority voting at the fusion center and a global decision is returned to each CR user. A CR user transmits or stays silent according to the global decision and at each CR user the global decision is compared to the actual primary user activity, which is ascertained through an acknowledgment signal. In the training phase enough information about the surrounding environment, i.e., the activity of PU and the behavior of each CR to that activity, is gathered and sensing classes formed. In the classification phase, each CR user compares its current sensing report to existing sensing classes and distance vectors are calculated. Based on quantitative variables, the posterior probability of each sensing class is calculated and the sensing report is classified into either representing presence or absence of PU. The quantitative variables used for calculating the posterior probability are calculated through K-nearest neighbor algorithm. These local decisions are then combined at the fusion center using a novel decision combination scheme, which takes into account the reliability of each CR user. The CR users then transmit or stay silent according to the global decision. Simulation results show that our proposed scheme outperforms conventional spectrum sensing schemes, both in fading and in nonfading environments, where performance is evaluated using metrics such as the probability of detection, total probability of error, and the ability to exploit data transmission opportunities.

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