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USENIX Security2017Top-tier venue

Malton: Towards On-Device Non-Invasive Mobile Malware Analysis for ART

Lei Xue, Yajin Zhou, Ting Chen, Xiapu Luo, Guofei Gu

2017Year
81Citations
15Top-tier citations

Abstract

It's an essential step to understand malware's behaviors for developing effective solutions. Though a number of systems have been proposed to analyze Android malware, they have been limited by incomplete view of inspection on a single layer. What's worse, various new techniques (e.g., packing, anti-emulator, etc.) employed by the latest malware samples further make these systems ineffective. In this paper, we propose Malton, a novel on-device non-invasive analysis platform for the new Android runtime (i.e., the ART runtime). As a dynamic analysis tool, Malton runs on real mobile devices and provides a comprehensive view of malware's behaviors by conducting multi-layer monitoring and information flow tracking, as well as efficient path exploration. We have carefully evaluated Malton using real-world malware samples. The experimental results showed that Malton is more effective than existing tools, with the capability to analyze sophisticated malware samples and provide a comprehensive view of malicious behaviors of these samples.

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