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dc.contributor.advisorMcEachen, John C.
dc.contributor.advisorThulasiraman, Preetha
dc.contributor.authorJones, Patrick
dc.date.accessioned2019-02-07T22:50:42Z
dc.date.available2019-02-07T22:50:42Z
dc.date.issued2017-12
dc.identifier.urihttp://hdl.handle.net/10945/61188
dc.description.abstractThis thesis investigates the feasibility of authenticating a user through a behavioral biometric signature from smartphone accelerometer data. Using a Samsung Galaxy S7, acceleration in relation to the necessary equilibrium, postural state for a subject to orient a smartphone in order to read a headline article was measured and recorded by the MATLAB Mobile application. Twenty subjects—1 known and 19 unknown—were used in the creation of a MATLAB machine-learning classifier. The classifier accurately distinguished an unknown subject from the known subject. Recommendations for future work include repeating the experiment with the latest smartphone devices as available, incorporating different sensors available to the “MATLAB Mobile App,” and introducing noise to spoof the known user.en_US
dc.description.urihttp://archive.org/details/authenticatingkn1094561188
dc.publisherMonterey, CA; Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleAuthenticating a Known User Through Behavioral Biometrics Using a Smartphone Accelerometeren_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineering (ECE)
dc.subject.authorsmartphoneen_US
dc.subject.authorauthenticationen_US
dc.subject.authorbehavioral biometricsen_US
dc.subject.authoraccelerometeren_US
dc.subject.authorAndroiden_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Electrical Engineeringen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineElectrical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.identifier.thesisid28738


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