论文标题

SIAT:一种系统的组成通信分析技术,用于检测对Android的威胁

SIAT: A Systematic Inter-Component Communication Analysis Technology for Detecting Threats on Android

论文作者

Hu, Yupeng, Jin, Zhe, Li, Wenjia, Xiang, Yang, Zhang, Jiliang

论文摘要

在本文中,我们介绍了由两个关键模块组成的系统间组件通信分析技术(SIAT)的设计和实现:\ emph {monitor}和\ emph {Analyszer}。作为框架层的Android操作系统的扩展,\ emph {Monitor}是第一次尝试修改命名标记方法,即在方法级别和文件级时都将其命名为TaintDroid,以将其迁移到App-Pair ICC路径通过SystemWide the SystemWIDE tracing and contion the contection systemsWide tracing and contion content content contection和Control Control和Control Flow content content content content the Systemwide tracing and Contract content。通过接管\ emph {Monitor}提供的污染日志,\ emph {Analyzer}可以构建使用检测算法和预定义规则来识别特定威胁模型的准确和集成的ICC模型。同时,我们采用模型的通缩技术来提高\ emph {Analyzer}的效率。我们使用Android开源项目实施SIAT,并通过对知名数据集和现实世界应用程序进行广泛的实验来评估其性能。实验结果表明,与最先进的方法相比,SIAT可以以1.0的精度获得约25 \%$ \ sim $ 200 \%的精度提高,并以0.98的召回率,而费用以可忽略不计的运行时开销。此外,SIAT可以确定两个未公开的案例,即绕过以前的技术无法检测到的,并且在现实世界中有很多恶意的ICC威胁,并在Google Play市场上有大量下载。

In this paper, we present the design and implementation of a Systematic Inter-Component Communication Analysis Technology (SIAT) consisting of two key modules: \emph{Monitor} and \emph{Analyzer}. As an extension to the Android operating system at framework layer, the \emph{Monitor} makes the first attempt to revise the taint tag approach named TaintDroid both at method-level and file-level, to migrate it to the app-pair ICC paths identification through systemwide tracing and analysis of taint in intent both at the data flow and control flow. By taking over the taint logs offered by the \emph{Monitor}, the \emph{Analyzer} can build the accurate and integrated ICC models adopted to identify the specific threat models with the detection algorithms and predefined rules. Meanwhile, we employ the models' deflation technology to improve the efficiency of the \emph{Analyzer}. We implement the SIAT with Android Open Source Project and evaluate its performance through extensive experiments on well-known datasets and real-world apps. The experimental results show that, compared to state-of-the-art approaches, the SIAT can achieve about 25\%$\sim$200\% accuracy improvements with 1.0 precision and 0.98 recall at the cost of negligible runtime overhead. Moreover, the SIAT can identify two undisclosed cases of bypassing that prior technologies cannot detect and quite a few malicious ICC threats in real-world apps with lots of downloads on the Google Play market.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源