Automated dynamic detection of self-hiding behavior
Baird, Luke Shan, Zhiyong Namboodiri, Vinod
Baird, Luke
Shan, Zhiyong
Namboodiri, Vinod
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2019-11
Type
Conference paper
Genre
Keywords
Tools,Navigation,Smart phones,Malware,Android,Humanoid robots,Testing
Subjects (LCSH)
Citation
L. Baird, Z. Shan and V. Namboodiri, "Automated Dynamic Detection of Self-Hiding Behavior," 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW), Monterey, CA, USA, 2019, pp. 87-91
Abstract
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.
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Publisher
IEEE
Journal
Book Title
Series
IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW);2019