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Detection of PUE Attack in CRN with Reduced Error in Location Estimation Using Novel Bat Algorithm

Detection of PUE Attack in CRN with Reduced Error in Location Estimation Using Novel Bat Algorithm

Aasia Rehman, Deo Prakash
Copyright: © 2017 |Volume: 6 |Issue: 2 |Pages: 25
ISSN: 2155-6261|EISSN: 2155-627X|EISBN13: 9781522514848|DOI: 10.4018/IJWNBT.2017070101
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MLA

Rehman, Aasia, and Deo Prakash. "Detection of PUE Attack in CRN with Reduced Error in Location Estimation Using Novel Bat Algorithm." IJWNBT vol.6, no.2 2017: pp.1-25. http://doi.org/10.4018/IJWNBT.2017070101

APA

Rehman, A. & Prakash, D. (2017). Detection of PUE Attack in CRN with Reduced Error in Location Estimation Using Novel Bat Algorithm. International Journal of Wireless Networks and Broadband Technologies (IJWNBT), 6(2), 1-25. http://doi.org/10.4018/IJWNBT.2017070101

Chicago

Rehman, Aasia, and Deo Prakash. "Detection of PUE Attack in CRN with Reduced Error in Location Estimation Using Novel Bat Algorithm," International Journal of Wireless Networks and Broadband Technologies (IJWNBT) 6, no.2: 1-25. http://doi.org/10.4018/IJWNBT.2017070101

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Abstract

Cognitive Radio Network Technology makes the efficient utilization of scarce spectrum resources by allowing the unlicensed users to opportunistically use the licensed spectrum. Cognitive Radio Network due to its flexible and open nature is vulnerable to a number of security attacks. This paper is mainly concerned with one of the physical layer attack called Primary User Emulation Attack and its detection. This paper solves the problem of PUE attack by localization technique based on TDOA measurements with reduced error in location estimation using a Novel Bat Algorithm (NBA). A number of cooperative secondary users are used for detecting the PUEA by comparing its estimated position with the known position of incumbent. The main goal of NBA is to minimize two fitness functions namely non-linear least square and the maximum likelihood in order to optimize the estimation error. After evaluation, simulation results clearly demonstrates that NBA results in reduced estimation error as compared to Taylor Series Estimation and Particle Swarm Optimization.

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