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Challenges in emulating sensor and resource-based state changes for Android malware detection

机译:在模拟传感器和基于资源的状态变化以进行Android恶意软件检测方面面临的挑战

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The increasing prevalence of mobile malware has driven the need for emulated, dynamic analysis techniques. Unfortunately, emulating mobile devices is nontrivial because of the different types of hardware features onboard (e.g., sensors) and the manner in which users interact with their devices as compared to traditional computing platforms. To evaluate this, our research focuses on the enumeration and comparison of multiple attributes and event values from sensors and dynamic resources on Android runtime environments, both from physical devices and online analysis services. Utilizing our results from enumeration, we develop two different Android applications that are successful in detecting and evading the emulated environments utilized by those mobile analysis services during execution. When ran on physical devices, the same applications successfully perform a pseudo-malware action and send device identifying information to our server.
机译:移动恶意软件的日益普及推动了对仿真,动态分析技术的需求。不幸的是,由于与传统计算平台相比,板载硬件类型(例如,传感器)的类型不同以及用户与其设备进行交互的方式,所以模拟移动设备是不平凡的。为了评估这一点,我们的研究重点是枚举和比较来自Android设备运行时环境中的传感器和动态资源(来自物理设备和在线分析服务)的多个属性和事件值。利用我们的枚举结果,我们开发了两个不同的Android应用程序,它们可以成功地检测和逃避那些移动分析服务在执行过程中使用的仿真环境。在物理设备上运行时,相同的应用程序会成功执行伪恶意软件操作,并将设备标识信息发送到我们的服务器。

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