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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2509.03442 |
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| _version_ | 1866909806315438080 |
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| author | Qian, Zhuoyun Miao, Hongyi Zhang, Cheng Hu, Qin Jiang, Yili Huang, Jiaqi Zhong, Fangtian |
| author_facet | Qian, Zhuoyun Miao, Hongyi Zhang, Cheng Hu, Qin Jiang, Yili Huang, Jiaqi Zhong, Fangtian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03442 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feature-Oriented IoT Malware Analysis: Extraction, Classification, and Future Directions Qian, Zhuoyun Miao, Hongyi Zhang, Cheng Hu, Qin Jiang, Yili Huang, Jiaqi Zhong, Fangtian Cryptography and Security 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. |
| title | Feature-Oriented IoT Malware Analysis: Extraction, Classification, and Future Directions |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2509.03442 |