Feature-Oriented IoT Malware Analysis: Extraction, Classification, and Future Directions

• URL: http://arxiv.org/abs/2509.03442v2
• Date: Thu, 25 Sep 2025 23:14:20 GMT
• Title: Feature-Oriented IoT Malware Analysis: Extraction, Classification, and Future Directions
• Authors: Zhuoyun Qian, Hongyi Miao, Cheng Zhang, Qin Hu, Yili Jiang, Jiaqi Huang, Fangtian Zhong,
• Abstract summary: This survey provides a comprehensive review of feature extraction techniques for IoT malware analysis.<n>We first examine static and dynamic feature extraction methods, followed by hybrid approaches.<n>We then explore feature representation strategies based on graph learning.
• Score: 9.266194874288507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As IoT devices continue to proliferate, their reliability is increasingly constrained by security concerns. In response, researchers have developed diverse malware analysis techniques to detect and classify IoT malware. These techniques typically rely on extracting features at different levels from IoT applications, giving rise to a wide range of feature extraction methods. However, current approaches still face significant challenges when applied in practice. This survey provides a comprehensive review of feature extraction techniques for IoT malware analysis from multiple perspectives. We first examine static and dynamic feature extraction methods, followed by hybrid approaches. We then explore feature representation strategies based on graph learning. Finally, we compare the strengths and limitations of existing techniques, highlight open challenges, and outline promising directions for future research.

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