Automated Execution Control and Dynamic Behavior Monitoring for Android (TM) Applications

M. Louw, Marc Krull, Tavaris J. Thomas, R. Cathey, Gregory L. Frazier, Mike Weber
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引用次数: 2

Abstract

We explore techniques for eliciting a behavioral description from an Android smartphone app in a controlled manner. A description of app behavior is useful for performing subsequent analysis such as model checking, for example to verify the app satisfies a set of desirable security properties. Our solution is to dynamically execute the app in a customized version of the Android SDK emulator, which provides many of an app's inputs as responses to invoked API calls. A more focused set of input values computed offline are then injected to the app via hooks introduced into the Android API implementation. To dynamically monitor app behavior, we instrument the app bytecode to record control and data flows during execution. We also instrument the Android API to record all of the app's inputs and outputs. We have used this technique on the DARPA Automated Program Analysis for Cybersecurity (APAC) program to reveal hidden, triggerable attacks in independently developed challenge apps. Our framework for extracting app behavior is part of Droid Reasoning, Analysis, and Protection Engine (DRAPE), an integrated, semi-automated app behavior analysis system capable of discovering hidden malware in Android apps.
Android (TM)应用程序的自动执行控制和动态行为监控
我们探索了从Android智能手机应用程序中以受控方式引出行为描述的技术。应用程序行为的描述对于执行后续分析(例如模型检查)非常有用,例如验证应用程序是否满足一组理想的安全属性。我们的解决方案是在Android SDK模拟器的定制版本中动态执行应用程序,该模拟器提供了许多应用程序的输入作为调用API调用的响应。然后通过引入Android API实现的钩子将离线计算的更集中的输入值集注入到应用程序中。为了动态监控应用程序的行为,我们测量了应用程序字节码来记录执行过程中的控制和数据流。我们还仪器Android API记录所有的应用程序的输入和输出。我们在DARPA网络安全自动化程序分析(APAC)项目中使用了这种技术,以揭示独立开发的挑战应用程序中隐藏的、可触发的攻击。我们提取应用程序行为的框架是Droid推理、分析和保护引擎(DRAPE)的一部分,这是一个集成的、半自动化的应用程序行为分析系统,能够发现Android应用程序中隐藏的恶意软件。
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