WHYPER: Towards Automating Risk Assessment of Mobile Applications

411 citations recorded by the source corpus.

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The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “In WHYPER [Pandita et al. 2013], the app permissions were acquired through the market and Natural Language Processing was implemented to determine why each permission was requested by parsing the app description.”
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, background): “WHYPER: Towards Automating Risk Assessment of Mobile Applications.”
Checking app behavior against app descriptions (2014, methodology): “Most related to our work is the WHYPER framework of Pandita et al. [19].”
TTPDrill: Automatic and Accurate Extraction of Threat Actions from Unstructured Text of CTI Sources (2017, methodology): “� e works in [23] [24] analyzed Android application descriptions Using NLP to infer the permissions that they require.”
FeatureSmith: Automatically Engineering Features for Malware Detection by Mining the Security Literature (2016, background): “Pandita et al. [34] proposed a framework for identifying permissions needed from Android app descriptions.”
A Survey of App Store Analysis for Software Engineering (2017, unknown): “[177] presented the WHYPER system for auto-”
DroidDetector: Android Malware Characterization and Detection Using Deep Learning (2016, unknown): “In general, permission-based approaches are developed primarily for risk assessment[7–10] rather than malware detection.”