Vulnerability Evaluation Method through Correlation Analysis of Android Applications

Abstract

Due to people in companies use mobile devices to access corporate data, attackers targeting corporate data use vulnerabilities in mobile devices. Most vulnerabilities in applications are caused by the carelessness of developers, and confused deputy attacks and data leak attacks using inter-application vulnerabilities are possible. These vulnerabilities are difficult to find through the single-application diagnostic tool that is currently being studied. This paper proposes a process to automate the decompilation of all the applications on a user’s mobile device and a mechanism to find inter-application vulnerabilities. The mechanism generates a list and matrix, detailing the vulnerabilities in the mobile device. The proposed mechanism is validated through an experiment on an actual mobile device with four installed applications, and the results show that the mechanism can accurately capture all application risks as well as inter-application risks. Through this mechanism, users can expect to find the risks in their mobile devices in advance and prevent damage.

Exposed! A case study on the vulnerability-proneness of Google Play Apps

“…Most of these features are of (i) numeric (i.e., Description Length, Photos, APK Size, Average Rating, Number of Raters and Number of Permissions), (ii) nominal (i.e., Play Store Category and Number of Installs), or (iii) boolean (i.e., Contains Ads, In App Purchase, and each specific Permission in the list) types. Note that, while the relationships between the individual features in Table 2 and security risks have been extensively studied in prior research [29,51,92,95,97], we leverage findings from these previous studies for selecting significant features to train machine learning algorithms.…”