LeakSemantic：识别移动应用中的异常敏感网络传输
LeakSemantic: Identifying Abnormal Sensitive Network Transmissions in Mobile Applications
摘要：
移动应用程序（app）经常通过网络传输各种意图的敏感数据。需要一些传输来实现应用程序的功能。然而，与恶意接收者的传输可能导致隐私泄露，并且倾向于隐秘地行为以逃避检测。问题是双重的：如何在移动应用程序中揭露敏感传输，以及如何确定敏感传输是否合法？在本文中，我们提出了LeakSemantic，这是一个可以自动定位移动应用程序中异常敏感网络传输的框架。LeakSemantic由一个混合程序分析组件和一个机器学习组件组成。我们的程序分析组件结合了静态分析和动态分析，可精确识别敏感的变速器。与现有的污点分析方法相比，LeakSemantic实现了更高的准确性，误报更少，并且能够收集每次传输的网络流量等运行时数据。基于从运行时数据导出的特征，构建机器学习分类器以进一步区分合法和非法披露。实验表明，LeakSemantic在1404个应用程序的2279个敏感连接上达到了91%的准确率。
英文摘要：
Mobile applications (apps) often transmit sensitive data through network with various intentions. Some transmissions are needed to fulfill the app's functionalities. However, transmissions with malicious receivers may lead to privacy leakage and tend to behave stealthily to evade detection. The problem is twofold: how does one unveil sensitive transmissions in mobile apps, and given a sensitive transmission, how does one determine if it is legitimate? In this paper, we propose LeakSemantic, a framework that can automatically locate abnormal sensitive network transmissions from mobile apps. LeakSemantic consists of a hybrid program analysis component and a machine learning component. Our program analysis component combines static analysis and dynamic analysis to precisely identify sensitive transmissions. Compared to existing taint analysis approaches, LeakSemantic achieves better accuracy with fewer false positives and is able to collect runtime data such as network traffic for each transmission. Based on features derived from the runtime data, machine learning classifiers are built to further differentiate between the legal and illegal disclosures. Experiments show that LeakSemantic achieves 91% accuracy on 2279 sensitive connections from 1404 apps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
