Collusive Data Leak and More: Large-scale Threat Analysis of Inter-app Communications

Amiangshu Bosu, Fang Liu, D. Yao, G. Wang
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引用次数: 124

Abstract

Inter-Component Communication (ICC) provides a message passing mechanism for data exchange between Android applications. It has been long believed that inter-app ICCs can be abused by malware writers to launch collusion attacks using two or more apps. However, because of the complexity of performing pairwise program analysis on apps, the scale of existing analyses is too small (e.g., up to several hundred) to produce concrete security evidence. In this paper, we report our findings in the first large-scale detection of collusive and vulnerable apps, based on inter-app ICC data flows among 110,150 real-world apps. Our system design aims to balance the accuracy of static ICC resolution/data-flow analysis and run-time scalability. This large-scale analysis provides real-world evidence and deep insights on various types of inter-app ICC abuse. Besides the empirical findings, we make several technical contributions, including a new open-source ICC resolution tool with improved accuracy over the state-of-the-art, and a large database of inter-app ICCs and their attributes.
合谋数据泄露等:应用间通信的大规模威胁分析
组件间通信(Inter-Component Communication, ICC)为Android应用程序之间的数据交换提供了一种消息传递机制。长期以来,人们一直认为,恶意软件编写者可能会滥用应用程序间的icc,利用两个或更多应用程序发动串通攻击。然而,由于在应用程序上执行成对程序分析的复杂性,现有分析的规模太小(例如,多达几百个),无法产生具体的安全证据。在本文中,我们报告了我们在第一次大规模检测串通和易受攻击的应用程序中的发现,基于110,150个现实世界应用程序的应用间ICC数据流。我们的系统设计旨在平衡静态ICC分辨率/数据流分析的准确性和运行时可扩展性。这种大规模的分析提供了真实世界的证据和对各种类型的应用程序间滥用ICC的深刻见解。除了实证研究结果,我们还在技术上做出了一些贡献,包括一个新的开源ICC分辨率工具,该工具的精度比最新技术更高,以及一个大型的应用程序间ICC及其属性数据库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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