Android is among the most targeted platform by attackers. While attackers are
improving their techniques, traditional solutions based on static and dynamic
analysis have been also evolving. In addition to the application code, Android
applications have some metadata that could be useful for security analysis of
applications. Unlike traditional application distribution mechanisms, Android
applications are distributed centrally in mobile markets. Therefore, beside
application packages, such markets contain app information provided by app
developers and app users. The availability of such useful textual data together
with the advancement in Natural Language Processing (NLP) that is used to
process and understand textual data has encouraged researchers to investigate
the use of NLP techniques in Android security. Especially, security solutions
based on NLP have accelerated in the last 5 years and proven to be useful. This
study reviews these proposals and aim to explore possible research directions
for future studies by presenting state-of-the-art in this domain. We mainly
focus on NLP-based solutions under four categories: description-to-behaviour
fidelity, description generation, privacy and malware detection.

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Author Of this post: <a href="">Sevil Sen</a>, <a href="">Burcu Can</a>

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