Publication Date

Spring 2018

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

Abstract

Most Android smartphone apps are free; in order to generate revenue, the app developers embed ad libraries so that advertisements are displayed when the app is being used. Billions of dollars are lost annually due to ad fraud. In this research, we propose a machine learning based scheme to detect Android adware based on static and dynamic features. We collect static features from the manifest file, while dynamic features are obtained from network traffic. Using these features, we initially classify Android applications into broad categories (e.g., adware and benign) and then further classify each application into a more specific family. We employ a variety of machine learning techniques including neural networks, random forests, adaboost and support vector machines.

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