SpyDroid:一个在Android上使用多个实时恶意软件检测器的框架

Shahrear Iqbal, Mohammad Zulkernine
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引用次数: 12

摘要

安卓已经成为下一代智能设备的领先操作系统。因此,Android恶意软件的数量也直线上升。已经提出了许多动态分析技术来检测Android恶意软件。然而,这些技术很少在用户设备上使用实时监控,因为Android不向第三方应用程序提供低级信息。此外,有些技术比其他技术更有效地检测特定的恶意软件类别。因此,终端用户可以通过安装多种恶意软件检测技术而受益。在本文中,我们提出了SpyDroid,一个实时恶意软件检测框架,可以容纳来自第三方(例如,研究人员和防病毒供应商)的多个检测器,并允许有效和可控的实时监控。SpyDroid由两个操作系统模块(监控和检测)组成,并支持应用层子探测器。子检测器是常规的Android应用程序,它使用监控模块监控和分析不同的运行时信息,并将发现报告给检测模块。检测模块决定何时将应用程序标记为恶意软件。研究人员和反病毒供应商现在可以通过应用市场发布他们的技术,最终用户可以根据需要安装任意数量的子探测器。我们使用Android开源项目(AOSP)实现了SpyDroid,我们对包含4,965个应用程序的数据集进行的实验表明,来自多个子检测器的决策可以显着提高真实设备上的恶意软件检测率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SpyDroid: A Framework for Employing Multiple Real-Time Malware Detectors on Android
Android has become the leading operating system for next-generation smart devices. Consequently, the number of Android malware has also skyrocketed. Many dynamic analysis techniques have been proposed to detect Android malware. However, very few of these techniques use real-time monitoring on user devices as Android does not provide low-level information to third-party apps. Moreover, some techniques detect a specific malware class more effectively than others. Therefore, end users can be benefited by installing multiple malware detection techniques. In this paper, we propose SpyDroid, a real-time malware detection framework that can accommodate multiple detectors from third-parties (e.g., researchers and antivirus vendors) and allows efficient and controlled real-time monitoring. SpyDroid consists of two operating system modules (monitoring and detection) and supports application layer sub-detectors. Sub-detectors are regular Android applications that monitor and analyze different runtime information using the monitoring module and they report the detection module about their findings. The detection module decides when to mark an app as malware. Researchers and antivirus vendors can now publish their techniques via app markets and end users can install any number of sub-detectors as they require. We have implemented SpyDroid using the Android Open Source Project (AOSP) and our experiments with a dataset containing 4,965 apps show that decisions from multiple sub-detectors can increase the malware detection rate significantly on a real device.
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