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Open AccessProceedings Article

Ninja: Towards Transparent Tracing and Debugging on {ARM}

TLDR
NINJA is proposed, a transparent malware analysis framework on ARM platform with low artifacts that leverages a hardware-assisted isolated execution environment TrustZone to transparently trace and debug a target application with the help of Performance Monitor Unit and Embedded Trace Macrocell.
Abstract
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 TrustZone 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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Citations
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Orpheus: Enforcing Cyber-Physical Execution Semantics to Defend Against Data-Oriented Attacks

TL;DR: This work proposes Orpheus, a security methodology for defending against data-oriented attacks by enforcing cyber-physical execution semantics of a control program, and presents a new program behavior model, i.e., the event-aware finite-state automaton (eFSA), which takes advantage of theevent-driven nature of control programs and incorporates event checking in anomaly detection.
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Understanding the Security of ARM Debugging Features

TL;DR: This paper performs a comprehensive security analysis of the ARM debugging features, and summarizes the security and vulnerability implications, and craft Nailgun attack, which obtains sensitive information and achieves arbitrary payload execution in a high-privileged mode from a low-privilege mode via misusing the debugging features.
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Droid-AntiRM: Taming Control Flow Anti-analysis to Support Automated Dynamic Analysis of Android Malware

TL;DR: Droid-AntiRM is designed and implemented, a new approach seeking to tame anti-analysis automatically and improve automated dynamic analysis, and has good efficiency to perform large-scale analysis.
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Preliminary Study of Trusted Execution Environments on Heterogeneous Edge Platforms

TL;DR: The experiments show that the performance overhead introduced by the TEEs is low, which indicates that integrating these Tees into the edge nodes can efficiently mitigate security loopholes with a low-performance overhead.

DROIDSCRIBE: Classifying android malware based on runtime behavior

TL;DR: In this article, the authors use machine learning to automatically classify Android malware samples into families with high accuracy, while observing only their runtime behavior, focusing exclusively on dynamic analysis of runtime behavior to provide a clean point of comparison.
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