Undergraduate Student Works
Second Advisor Name
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 and efficient, providing tools usable by the Android marketplace for enhanced security screening.
The Sixth National Workshop for REU Research in Networking and Systems
Scholarly Commons Citation
Baird, L. (2019). Automated Dynamic Detection of Self-Hiding Behaviors. , (). Retrieved from https://commons.erau.edu/student-works/149