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Mass-market mobile security threats have increased recently due to the growthof mobile technologies and the popularity of mobile devices. Accordingly,techniques have been introduced for identifying, classifying, and defendingagainst mobile threats utilizing static, dynamic, on-device, off-device, andhybrid approaches. In this paper, we contribute to the mobile security defenseposture by introducing Andro-profiler, a hybrid behavior based analysis andclassification system for mobile malware. Andro-profiler classifies malware byexploiting the behavior profiling extracted from the integrated system logsincluding system calls, which are implicitly equivalent to distinct behaviorcharacteristics. Andro-profiler executes a malicious application on an emulatorin order to generate the integrated system logs, and creates human-readablebehavior profiles by analyzing the integrated system logs. By comparing thebehavior profile of malicious application with representative behavior profilefor each malware family, Andro-profiler detects and classifies it into malwarefamilies. The experiment results demonstrate that Andro-profiler is scalable,performs well in detecting and classifying malware with accuracy greater than$98\%$, outperforms the existing state-of-the-art work, and is capable ofidentifying zero-day mobile malware samples.

Andro-profiler: Detecting and Classifying Android Malware based on Behavioral Profiles