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Malicious users detection in collaborative spectrum sensing using statistical tests

Malicious users detection in collaborative spectrum sensing using statistical tests

Arshad, Kamran (2012) Malicious users detection in collaborative spectrum sensing using statistical tests. In: The Fourth International Conference on Ubiquitous and Future Networks (ICUFN 2012). Institute of Electrical and Electronics Engineers, Inc., Piscataway, NJ, USA, pp. 109-113. ISBN 9781467313780 (doi:10.1109/ICUFN.2012.6261674)

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Abstract

Collaboration among cognitive radios has been extensively studied in the past and widely accepted as a viable approach to improve spectrum sensing reliability. Data fusion in collaborative spectrum sensing rely on the information received from cognitive radios. It has been shown in literature that the performance of collaborative spectrum sensing degrades significantly in the presence of even a single malicious user. In this paper, a new scheme to detect and eliminate malicious users in collaborative spectrum sensing is proposed. Our method is based on the Grubb's test and is able to detect and eliminate observations of multiple malicious users. Simulation results show that the proposed scheme has much higher detection probability in the presence of malicious users especially for the case when the received signal to noise ratio is low.

Item Type: Conference Proceedings
Title of Proceedings: The Fourth International Conference on Ubiquitous and Future Networks (ICUFN 2012)
Additional Information: [1] This paper was first presented at The Fourth International Conference on Ubiquitous and Future Networks (ICUFN 2012) held from 4-6 July 2012 in Phuket, Thailand. [2] ISBN: 9781467313780; 9781467313773
Uncontrolled Keywords: sensor fusion, telecommunication network reliability, telecommunication security, Grubb's test, cognitive radios, collaborative spectrum sensing, data fusion, detection probability, malicious users detection, multiple malicious users, spectrum sensing reliability, statistical tests, cognitive radio, collaboration, equations, Gaussian distribution, mathematical model, sensors, signal to noise ratio
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Pre-2014 Departments: School of Engineering
School of Engineering > Mobile & Wireless Communications Research Laboratory
Related URLs:
Last Modified: 14 Oct 2016 09:27
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/11308

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