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arXiv · 2509.10709

Feature-Centric Approaches to Android Malware Analysis: A Survey

Shama Maganur·Yili Jiang·Jiaqi Huang·Fangtian Zhong
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Abstract

Sophisticated malware families exploit the openness of the Android platform to infiltrate IoT networks, enabling large-scale disruption, data exfiltration, and denial-of-service attacks. This systematic literature review (SLR) examines cutting-edge approaches to Android malware analysis with direct implications for securing IoT infrastructures. We analyze feature extraction techniques across static, dynamic, hybrid, and graph-based methods, highlighting their trade-offs: static analysis offers efficiency but is easily evaded through obfuscation; dynamic analysis provides stronger resistance to evasive behaviors but incurs high computational costs, often unsuitable for lightweight IoT devices; hybrid approaches balance accuracy with resource considerations; and graph-based methods deliver superior semantic modeling and adversarial robustness. This survey contributes a structured comparison of existing methods, exposes research gaps, and outlines a roadmap for future directions to enhance scalability, adaptability, and long-term security in IoT-driven Android malware detection.

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Shama Maganur, Yili Jiang, Jiaqi Huang, Fangtian Zhong. 2025-09-12. Feature-Centric Approaches to Android Malware Analysis: A Survey. https://arxiv.org/abs/2509.10709

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