Publication Date

Spring 2017

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Mark Stamp

Second Advisor

Robert Chun

Third Advisor

Chris Pollett

Keywords

Multi-approach Masquerade Detection, Mobile

Abstract

A masquerade is an attack where the attacker avoids detection by impersonating an authorized user of a system. In this research we consider the problem of masquerade detection on mobile devices. Our goal is to improve on previous work by considering more features and a wide variety of machine learning techniques. Our approach consists of verifying the authenticity of users based on individual features and combinations of features for all users to determine which features contribute the most to masquerade detection. Also, we determine which of the two approaches - the combination of features or using individual features has performed better.

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