CuriousDroid: Automated User Interface Interaction for Android Application Analysis Sandboxes
CuriousDroid: Automated User Interface Interaction for Android Application Analysis Sandboxes
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DOI：
10.1007/978-3-662-54970-4_13
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发表时间：
2016-02
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影响因子：
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通讯作者：
Patrick Carter;Collin Mulliner;Martina Lindorfer;William K. Robertson;E. Kirda
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文献类型：
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作者：
Patrick Carter;Collin Mulliner;Martina Lindorfer;William K. Robertson;E. Kirda
近年来，移动计算在市场份额和计算能力方面经历了巨大的增长。因此，移动恶意软件正变得更加复杂和普遍，这导致了对动态沙盒的研究，作为检测恶意应用程序的广泛方法。然而，Android应用程序的事件驱动性质使得自动生成确定性和智能用户交互以驱动分析主题和提高代码覆盖率的能力变得至关重要。在本文中，我们介绍了CuriousDroid，这是一个用于在智能的、类似用户的管理器中练习Android应用程序用户界面的自动化系统。CuriousDroid的运行方式是动态分解应用程序用户界面，并创建一个针对当前用户布局量身定做的基于上下文的交互模型。我们将CuriousDroid与著名的Android沙盒Andrabis集成在一起，并对来自不同数据集的38872个应用程序进行了大规模评估。我们的评估表明，在端到端样本分类以及在运行时引发的行为的原始数量增加方面都有显著的改进。
Mobile computing has experienced enormous growth in market share and computational power in recent years. As a result, mobile malware is becoming more sophisticated and more prevalent, leading to research into dynamic sandboxes as a widespread approach for detecting malicious applications. However, the event-driven nature of Android applications renders critical the capability to automatically generate deterministic and intelligent user interactions to drive analysis subjects and improve code coverage. In this paper, we present CuriousDroid, an automated system for exercising Android application user interfaces in anintelligent,user-likemanner. CuriousDroid operates by decomposing application user interfaces on-the-fly and creating a context-based model for interactions that is tailored to the current user layout. We integrated CuriousDroid with Andrubis, a well-known Android sandbox, and conducted a large-scale evaluation of 38,872 applications taken from different data sets. Our evaluation demonstrates significant improvements in both end-to-end sample classification as well as increases in the raw number of elicited behaviors at runtime.