Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/57282
Title: An Eclat algorithm based energy detection for cognitive radio networks
Contributor(s): Jin, Fan (author); Varadharajan, Vijay (author); Tupakula, Udaya  (author)orcid 
Publication Date: 2017
DOI: 10.1109/Trustcom/BigDataSE/ICESS.2017.358
Handle Link: https://hdl.handle.net/1959.11/57282
Abstract: 

Cognitive radio (CR) can improve the utilization of the spectrum by making use of licensed spectrum in an opportunistic manner. The sensing reports from all the CR nodes are sent to a Fusion Centre (FC) which aggregates these reports and takes decision about the presence of the PU, based on some decision rules. Such a collaborative sensing mechanism forms the foundation of any centralised CRN. However, this collaborative sensing mechanism provides more opportunities for malicious users (MUs) hiding in the legal users to launch spectrum sensing data falsification (SSDF) attacks. In an SSDF attack, some malicious users intentionally report incorrect local sensing results to the FC and disrupt the global decision-making process. To mitigate SSDF attacks, an Eclat algorithm based detection strategy is proposed in this paper for finding out the colluding malicious nodes. Simulation results show that the sensing performance of the scheme is better than the traditional majority based voting decision in the presence of SSDF attacks.

Publication Type: Conference Publication
Conference Details: 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, Australia, 1st-4th August, 2017
Source of Publication: Proceedings: the 16th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, the 11th IEEE International Conference on Big Data Science and Engineering, the 14th IEEE International Conference on Embedded Software and Systems, p. 1096-1102
Publisher: Institute of Electrical and Electronics Engineers
Place of Publication: United States of America
Fields of Research (FoR) 2020: 460407 System and network security
Socio-Economic Objective (SEO) 2020: 220405 Cybersecurity
HERDC Category Description: E1 Refereed Scholarly Conference Publication
Appears in Collections:Conference Publication
School of Science and Technology

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