• Corpus ID: 2726752

DroidNative: Semantic-Based Detection of Android Native Code Malware

@article{Alam2016DroidNativeSD,
  title={DroidNative: Semantic-Based Detection of Android Native Code Malware},
  author={Shahid Alam and Zhengyang Qu and Ryan D. Riley and Yan Chen and Vaibhav Rastogi},
  journal={ArXiv},
  year={2016},
  volume={abs/1602.04693}
}

• Shahid Alam, Zhengyang Qu, +2 authors Vaibhav Rastogi
• Published 15 February 2016
• Computer Science
• ArXiv

According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively ~99% on the Android platform. Malware writers are applying stealthy mutations (obfuscations) to create malware variants, thwarting detection by signature based detectors. In addition, the plethora of more sophisticated detectors making use of static analysis techniques to detect such variants operate only at the bytecode level, meaning that malware embedded in native code goes undetected. A recent study shows that 86% of the most popular Android applications contain native code, making this a plausible threat. This paper proposes DroidNative, an Android malware detector that uses specific control flow patterns to reduce the effect of obfuscations, provides automation and platform independence, and as far as we know is the first system that operates at the Android native code level, allowing it to detect malware embedded in both native code and bytecode. When tested with traditional malware variants it achieves a detection rate (DR) of 99.48%, compared to academic and commercial tools' DRs that range from 8.33% -- 93.22%. When tested with a dataset of 2240 samples DroidNative achieves a DR of 99.16%, a false positive rate of 0.4% and an average detection time of 26.87 sec/sample.