Identification as a deterrent for security enhancement in cognitive radio networks. Issue 6 (1st November 2017)

• Record Type: Journal Article
• Authors: Fakhrudeen, Ahmed M.; Alani, Omar Y.
• Is Part Of: IET networks. Volume 6:Issue 6(2017)
• Page Start: 193
• Page End: 202
• Publication Date: 2017-11-01
• Subjects: cognitive radio -- telecommunication security -- closed circuit television; cognitive radio networks -- security enhancement -- CRN -- primary user emulation attack -- PUEA deterrent algorithm -- closed‐circuit television; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2017.0036
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : Cognitive radio networks (CRNs) are prone to emerging coexistence security threats such as primary user emulation attack (PUEA). Specifically, a malicious CRN may mimic licensees' [primary users (PUs)] signal characteristics to force another CRN to vacate its channels thinking that PUs have returned. While existing schemes are promising to some extent on detecting PUEAs, they are not able to prevent the attacks. In this article, the authors propose a PUEA deterrent (PUED) algorithm that can provide PUEAs' commission details: offender CRNs and attacks' time and bandwidth. There are many similarities between PUED and closed‐circuit television (CCTV) in terms of: deterrence strategy, reason for use, surveillance characteristics, surveillance outcome, and operation site. According to the criminology literature, robust CCTV systems have shown a significant reduction in visible offences (e.g. vehicle theft), reducing crime rates by 80%. Similarly, PUED will contribute the same effectiveness in deterring PUEAs. Furthermore, providing PUEAs' details will prevent the network's cognitive engine from considering the attacks as real PUs, consequently avoiding devising unreliable spectrum models for the attacked channels. Extensive simulations show the effectiveness of the PUED algorithm in terms of improving CRNs' performance.