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dc.contributor.authorSezer, Sakiren
dc.contributor.authorYao, F.en
dc.contributor.authorYerima, Suleimanen
dc.contributor.authorKang, B.en
dc.date.accessioned2018-10-31T11:08:34Z
dc.date.available2018-10-31T11:08:34Z
dc.date.issued2017-10-19
dc.identifier.citationYao, F., Yerima, S. Y., Kang, B. and Sezer, S. (2017) Continuous Implicit Authentication for Mobile Devices based on Adaptive Neuro-Fuzzy Inference System. In: International Conference on Cyber Security and Protection of Digital Services (Cyber Security 2017): Proceedings, pp. 1-7, London, Uk, June 2017.en
dc.identifier.urihttps://pure.qub.ac.uk/portal/files/129533677/bare_conf.pdf
dc.identifier.urihttp://hdl.handle.net/2086/16937
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractAs mobile devices have become indispensable in modern life, mobile security is becoming much more important. Traditional password or PIN-like point-of-entry security measures score low on usability and are vulnerable to brute force and other types of attacks. In order to improve mobile security, an adaptive neuro-fuzzy inference system(ANFIS)-based implicit authentication system is proposed in this paper to provide authentication in a continuous and transparent manner. To illustrate the applicability and capability of ANFIS in our implicit authentication system, experiments were conducted on behavioural data collected for up to 12 weeks from different Android users. The ability of the ANFIS-based system to detect an adversary is also tested with scenarios involving an attacker with varying levels of knowledge. The results demonstrate that ANFIS is a feasible and efficient approach for implicit authentication with an average of 95% user recognition rate. Moreover, the use of ANFIS-based system for implicit authentication significantly reduces manual tuning and configuration tasks due to its self-learning capability.en
dc.language.isoenen
dc.publisherIEEEen
dc.subjectauthenticationen
dc.subjectneuro-fuzzyen
dc.subjectmobile securityen
dc.subjectimplicit authenticationen
dc.subjectartificial intelligenceen
dc.subjectfuzzy logicen
dc.subjectadaptive neuro-fuzzy inference systemen
dc.titleContinuous implicit authentication for mobile devices based on adaptive neuro-fuzzy inference systemen
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/cybersecpods.2017.8074846
dc.researchgroupCyber Technology Institute (CTI)en
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017en
dc.exception.reasonauthor was not DMU staff at time of publication, available from Queen's Uni Repositoryen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.exception.ref2021codes254aen


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