An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

doi:10.23919/JSEE.2020.000002

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (2): 243-251.doi: 10.23919/JSEE.2020.000002

• Electronics Technology • Previous Articles Next Articles

An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

Fang YE(), Ping BAI(), Yuan TIAN*()

School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China

Received:2019-01-14 Online:2020-04-30 Published:2020-04-30
Contact: Yuan TIAN E-mail:yefang0923@126.com;baiping0325@126.com;tianyuan347@126.com
About author:YE Fang was born in 1980. She received her B.S. and Ph.D. degrees in electrical information engineering from Harbin Engineering University in 2002 and 2006, respectively. During 2007-2008, she worked as a visiting scholar in University of Southampton. At present, she is an associate professor in Harbin Engineering University. Her research interests include cognitive radio network and radio resource management. E-mail: yefang0923@126.com|BAI Ping was born in 1994. She received her B.S. and M.S. degrees from Harbin Engineering University in 2016 and 2019, respectively. During her postgraduate period, she concentrated on cognitive radio spectrum sensing. At present, she is an algorithm engineer in Huawei company. E-mail: baiping0325@126.com|TIAN Yuan was born in 1978. She received her B.S and M.S. degrees from Harbin Engineering University in 2000 and 2006, respectively. At present, she is a lecturer in Harbin Engineering University. Her research interests include cognitive radio network and radio resource management. E-mail: tianyuan347@126.com
Supported by:
the National Natural Science Foundation of China(61701134);the National Natural Science Foundation of China(51809056);the Fundamental Research Funds for the Central Universities of China(HEUCFM180802);the National Key Research and Development Program of China(2016YFF0102806);the Natural Science Foundation of Heilongjiang Province, China(F2017004);This work was supported by the National Natural Science Foundation of China (61701134; 51809056), the Fundamental Research Funds for the Central Universities of China (HEUCFM180802), the National Key Research and Development Program of China (2016YFF0102806), and the Natural Science Foundation of Heilongjiang Province, China (F2017004)

Abstract

Abstract:

In cognitive radio networks, spectrum sensing is one of the most important functions to identify available spectrum for improving the spectrum utilization. Due to the open characteristic of the wireless electromagnetic environment, the wireless network is vulnerable to be attacked by malicious users (MUs), and spectrum sensing data falsification (SSDF) attack is one of the most harmful attacks on spectrum sensing performance. In this article, an algorithm based on the evidence theory and fuzzy entropy is proposed to resist SSDF attacks. In this algorithm, secondary users (SUs) obtain the corresponding degree of membership function and basic probability assignment function based on the local energy detection result. The new conflicting coefficient is calculated based on the evidence distance and classical conflicting coefficient, and the conflicting weight of the evidence is obtained. The fuzzy weight is calculated by the fuzzy entropy. The credibility weight is obtained by updating the credibility. On this basis, the probability assignment function of the evidence is corrected, and the final result is obtained by using the fusion formula. Simulation results show that the proposed algorithm has a higher detection probability and lower false alarm probability than other algorithms. It can effectively defend against SSDF attacks and improve the performance of spectrum sensing.

Key words: cooperative spectrum sensing, evidence theory, fuzzy entropy, spectrum sensing data falsification (SSDF)

Fang YE, Ping BAI, Yuan TIAN. An algorithm based on evidence theory and fuzzy entropy to defend against SSDF[J]. Journal of Systems Engineering and Electronics, 2020, 31(2): 243-251.

Figures/Tables 13

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An algorithm based on evidence theory and fuzzy entropy to defend against SSDF

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