Identifier

etd-01052017-211923

Degree

Master of Science (MS)

Department

Computer Science

Document Type

Thesis

Abstract

Malware targeting mobile devices is a pervasive problem in modern life and as such tools to detect and classify malware are of great value. This paper seeks to demonstrate the effectiveness of Deep Learning Techniques, specifically Convolutional Neural Networks, in detecting and classifying malware targeting the Android operating system. Unlike many current detection techniques, which require the use of relatively rigid features to aid in detection, deep neural networks are capable of automatically learning flexible features which may be more resilient to obfuscation. We present a parsing for extracting sequences of API calls which can be used to describe a hypothetical execution of a given application. We then show how to use this sequence of API calls to successfully classify Android malware using a Convolutional Neural Network.

Date

2016

Document Availability at the Time of Submission

Release the entire work immediately for access worldwide.

Committee Chair

Zhang, Jian

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