Engineering Commons^™

Advanced Security Analysis For Emergent Software Platforms, Mohannad Alhanahnah

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems.

This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the …

Automated Dynamic Detection Of Self-Hiding Behaviors, Luke Baird

Student Works

Certain Android applications, such as but not limited to malware, conceal their presence from the user, exhibiting a self-hiding behavior. Consequently, these apps put the user’s security and privacy at risk by performing tasks without the user’s awareness. Static analysis has been used to analyze apps for self-hiding behavior, but this approach is prone to false positives and suffers from code obfuscation. This research proposes a set of three tools utilizing a dynamic analysis method of detecting self-hiding behavior of an app in the home, installed, and running application lists on an Android emulator. Our approach proves both highly accurate …

Automated Dynamic Detection Of Self-Hiding Behavior In Android Apps, Luke Baird, Seth Rodgers

Student Works

Android applications that conceal themselves from a user, defined as exhibiting a “self-hiding behavior,” pose a threat to the user’s privacy, as these applications can live on a device undetected by the user. Malicious applications can do this to execute without being found by the user. Three lists are analyzed in particular—the home, running, and installed lists—as they are directly related to the typical Android app life cycle. Additionally, self-hiding behavior in the device admin list is analyzed due to the potential for catastrophic actions to be taken by device admin malware. This research proposes four dynamic analysis tools that …

Gaindroid: General Automated Incompatibility Notifier For Android Applications, Bruno Vieira Resende E Silva

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

With the ever-increasing popularity of mobile devices over the last decade, mobile apps and the frameworks upon which they are built frequently change. This rapid evolution leads to a confusing jumble of devices and applications utilizing differing features even within the same framework. For Android apps and devices, representing over 80% of the market share, mismatches between the version of the Android operating system installed on a device and the version of the app installed, can lead to several run-time crashes, providing a poor user experience.

This thesis presents GAINDroid, an analysis approach, backed with a classloader based program analyzer, …