Discovering inconsistencies between requested permissions and application metadata by using deep learning
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AbstractAndroid gives us opportunity to extract meaningful information from metadata. From the security point of view, the missing important information in metadata of an application could be a sign of suspicious application, which could be directed for extensive analysis. Especially the usage of dangerous permissions is expected to be explained in app descriptions. The permission-to-description fidelity problem in the literature aims to discover such inconsistencies between the usage of permissions and descriptions. This study proposes a new method based on natural language processing and recurrent neural networks. The effect of user reviews on finding such inconsistencies is also investigated in addition to application descriptions. The experimental results show that high precision is obtained by the proposed solution, and the proposed method could be used for triage of Android applications.
CitationAlecakir, H., Kabukcu, M., Can, B. and Sen, S. (2020) Discovering inconsistencies between requested permissions and application metadata by using deep learning, 2020 International Conference on Information Security and Cryptology (ISCTURKEY), DOI 10.1109/ISCTURKEY51113.2020.9308004
DescriptionThis is an accepted manuscript of an article published by IEEE in 2020 International Conference on Information Security and Cryptology (ISCTURKEY), available online at https://ieeexplore.ieee.org/document/9308004 The accepted version of the publication may differ from the final published version.
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/