To make smartphone authentication more convenient and encourage usage of more secure methods, we designed a system, LOBS, that would authenticate users by recognizing their behavior patterns. LOBS constructed behavioral signatures by examining visible WiFi networks, GPS location, accelerometer data, battery usage, and when the screen was turned off and on. It used a neural network trained on the user’s historical data to analyze the latest data and determine a trust score, measuring how likely that data was to be from the same person, and authenticating the user if it was high enough. We evaluated LOBS with a study that used data gathered from six people over a week. The results we obtained were too low for LOBS to be commercially marketable, but much higher than random chance.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Rueger, Derrek R
• Benson, Bryan Joshua
• Huchley, Benjamin

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-030718-173301

Advisor

• Agu, Emmanuel O.

Year

• 2018

Date created

• 2018-03-07

Resource type

• Major Qualifying Project

Major

• Computer Science

Rights statement

• In Copyright