머신러닝 기반의 자동화된 소스 싱크 분류 및 하이브리드 분석을 통한 개인정보 유출 탐지 방법

The Android framework allows apps to take full advantage of personal information through granting single permission,and does not determine whether the data being leaked is actual personal information. To solve these problems, we propose atool with static/dynamic analysis. The tool analyzes the Source and Sink used by the target app, to provide users withinformation on what personal information it used. To achieve this, we extracted the Source and Sink through Control FlowGraph and make sure that it leaks the user's privacy when there is a Source-to-Sink flow. We also used the sensitivepermission information provided by Google to obtain information from the sensitive API corresponding to Source and Sink. Finally, our dynamic analysis tool runs the app and hooks information from each sensitive API. In the hooked data, we gotinformation about whether user’s personal information is leaked through this app, and delivered to user. In this process, anautomated Source/Sink classification model was applied to collect latest Source/Sink information, and the we categorizedlatest release version of Android(9.0) with 88.5% accuracy. We evaluated our tool on 2,802 APKs, and found 850 APKsthat leak personal information.