| Title | Discovering Inconsistencies between Requested Permissions and Application Metadata by using Deep Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Alecakir, Huseyin, Kabukcu, Muhammet, Can, Burcu, Sen, Sevil |
| Conference Name | 2020 International Conference on Information Security and Cryptology (ISCTURKEY) |
| Keywords | android, compositionality, Deep Learning, description-to-permission fidelity, metadata, Metadata Discovery Problem, natural language processing, Predictive models, pubcrawl, Recurrent neural networks, resilience, Resiliency, Scalability, security, Semantics, smart phones, Task Analysis |
| Abstract | Android 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. |
| DOI | 10.1109/ISCTURKEY51113.2020.9308004 |
| Citation Key | alecakir_discovering_2020 |
Discovering Inconsistencies between Requested Permissions and Application Metadata by using Deep Learning