Authenticating a Known User Through Behavioral Biometrics Using a Smartphone Accelerometer

This 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.

Department dc:contributor.department

Electrical and Computer Engineering (ECE)

Grantor dc:publisher

Monterey, CA; Naval Postgraduate School

Year dc:date.issued

2017

Author dc:creator

• Jones, Patrick

Advisors dc:contributor.advisor

• McEachen, John C.
• Thulasiraman, Preetha

Statement dc:rights

• This 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.

Handle dc:identifier.uri

https://hdl.handle.net/10945/61188

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/61188