Feature-Centric Approaches to Android Malware Analysis: A Survey

• URL: http://arxiv.org/abs/2509.10709v1
• Date: Fri, 12 Sep 2025 21:55:26 GMT
• Title: Feature-Centric Approaches to Android Malware Analysis: A Survey
• Authors: Shama Maganur, Yili Jiang, Jiaqi Huang, Fangtian Zhong,
• Abstract summary: Sophisticated malware families exploit the openness of the Android platform to infiltrate IoT networks.<n>This review examines cutting-edge approaches to Android malware analysis with implications for securing IoT infrastructures.
• Score: 5.605292425841782
• License: http://creativecommons.org/licenses/by/4.0/
• 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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