The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a security analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

@inproceedings{paperbot4449,
  title = {Lightweight, obfuscation-resilient detection and family identification of Android malware},
  author = {Joshua Garcia and Mahmoud Hammad and Sam Malek},
  booktitle = {ICSE},
  year = {2018},
  doi = {10.1145/3180155.3182551}
}