Ninja: Towards Transparent Tracing and Debugging on ARM


Zhenyu Ning and Fengwei Zhang, Wayne State University


Existing malware analysis platforms leave detectable fingerprints like uncommon string properties in QEMU, signatures in Android Java virtual machine, and artifacts in Linux kernel profiles. Since these fingerprints provide the malware a chance to split its behavior depending on whether the analysis system is present or not, existing analysis systems are not sufficient to analyze the sophisticated malware. In this paper, we propose NINJA, a transparent malware analysis framework on ARM platform with low artifacts. NINJA leverages a hardware-assisted isolated execution environment Trust-Zone to transparently trace and debug a target application with the help of Performance Monitor Unit and Embedded Trace Macrocell. NINJA does not modify system software and is OS-agnostic on ARM platform. We implement a prototype of NINJA (i.e., tracing and debugging subsystems), and the experiment results show that NINJA is efficient and transparent for malware analysis.

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@inproceedings {203826,
author = {Zhenyu Ning and Fengwei Zhang},
title = {Ninja: Towards Transparent Tracing and Debugging on {ARM}},
booktitle = {26th USENIX Security Symposium (USENIX Security 17)},
year = {2017},
isbn = {978-1-931971-40-9},
address = {Vancouver, BC},
pages = {33--49},
url = {},
publisher = {USENIX Association},
month = aug

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