Data on Vulnerability Detection in Android

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ID: 5047
2019
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Abstract
The data in this article have been collaborated from mainly four sources- Google Playstore,11 Google Playstore https://play.google.com/store/apps. Wandoujia22 Wandoujia apps http://www.wandoujia.com/apps. (third party app store market), AMD33 AMD http://amd.arguslab.org/sharing. and Androzoo.44 Androzoo https://androzoo.uni.lu/access. 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.
Reference Key
garg2019datadata Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Garg, Shivi;Baliyan, Niyati;
Journal Data in brief
Year 2019
DOI
DOI not found
URL
http://www.sciencedirect.com/science/article/pii/S2352340918315774
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