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

Thomas Austin

Keywords

Code obfuscation, malware detectors

Abstract

Code obfuscation can make it challenging to detect malware in Android devices. Malware writers obfuscate the code of their programs by employing various techniques that attempt to hide the true purpose of the program. Malware detectors can use a number of features to classify a program as a malware. If the malware detector uses a feature that is obfuscated, then the malware detector will likely fail to classify the malware as malicious software. In this research, we obfuscate selected features of known malware and determine whether the malware can still be detected by a given detector. Using this approach, we show that we can effectively perform black box analysis of various malware detectors.

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