The data in this article have been collaborated from mainly four sources- Google Playstore, Wandoujia (third party app store market), AMD and Androzoo. These data include ~85,000 APKs (Android Package Kit), both malicious and benign from these data sources. Static and dynamic features are extracted from these APK files, and then supervised machines learning algorithms are employed for malware detection in Android. This data article also provides the Python code for data analysis. For feature extraction, a generic algorithm has also been incorporated, thereby, selecting important and relevant feature subset. Conclusive results obtained from this data set are further comprehended and interpreted in our latest research study "A Novel Parallel Classifier Scheme for Vulnerability Detection in Android" (Garg et al., 2018). This proved to be precious contribution for ensembling classifiers in machine learning to detect malware in Android.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6377051 | PMC |
| http://dx.doi.org/10.1016/j.dib.2018.12.038 | DOI Listing |
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