RegionDroid:基于运行时UI区域特征检测Android应用程序重新打包的工具

Shengtao Yue, Qingwei Sun, Jun Ma, Xianping Tao, Chang Xu, Jian Lu
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引用次数: 5

摘要

随着移动设备的快速发展,Android应用程序(app)被普遍使用。然而,攻击者将Android应用重新打包并投放到市场上,用于非法目的,这给Android生态系统带来了巨大的威胁。为了利用原创应用的受欢迎程度,他们保留了类似的软件行为来迷惑应用用户。此外,重新打包的应用程序可以被混淆或加密,以避免被检测到。此外,结合网络技术和本地元素构建的混合手机应用正成为开发者的首选。混合应用的结构与原生应用有很大不同,这给重新包装检测带来了巨大挑战。现有的工作在检测从混淆和加密的应用程序重新打包方面仍然有一些限制。此外,他们中很少有人能处理混合应用。在本文中,我们提出了一种基于从应用运行时UI轨迹中提取的应用UI区域的方法。我们还基于该方法实现了一个名为RegionDroid的工具。我们将RegionDroid应用于共369个应用程序的3个数据集。它成功地找到了数据集S1中所有98个混淆或加密的重新打包对。它在区分数据集S2中的另外114个商业应用程序方面也显示出良好的可信度。我们还在数据集S3中测试了我们的方法,其中包含157个混合应用程序,通过对它们进行配对比较,假阳性率为0.016%。
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RegionDroid: A Tool for Detecting Android Application Repackaging Based on Runtime UI Region Features
With the rapid development of mobile devices, Android applications (apps) are universally used. However, attackers repackage Android apps and release them to the markets for illegal purposes, which brings great threats to the Android ecosystem. To leverage the popularity of original apps, they keep similar software behaviors to confuse app users. Furthermore, repackaged apps can be obfuscated or encrypted to avoid being detected. Besides, hybrid mobile apps, built by combining web technology and native elements, are becoming a preferred choice for developers. The structure of hybrid apps differs a lot from that of native apps which would raise great challenges to repackaging detection. Existing works still have some limitations in detecting repackaging from obfuscated and encrypted apps. Besides, few of them can deal with hybrid apps. In this paper, we proposed an approach based on the app UI regions extracted from app's runtime UI traces. We also implement a tool named RegionDroid based on the approach. We apply RegionDroid to tree datasets with totally 369 apps. It successfully finds all the 98 obfuscated or encrypted repackaged pairs in dataset S1. It also shows good credibility in distinguishing another 114 commercial apps in dataset S2. We also test our approach in dataset S3 with 157 hybrid apps by comparing them pairwisely and the false positive rate is 0.016%.
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