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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.16835 |
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| _version_ | 1866911220192247808 |
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| author | Bai, Hongpeng Dong, Minhong Zhang, Yao Zhao, Shunzhe Zhang, Haobo Li, Lingyue Bai, Yude Xu, Guangquan |
| author_facet | Bai, Hongpeng Dong, Minhong Zhang, Yao Zhao, Shunzhe Zhang, Haobo Li, Lingyue Bai, Yude Xu, Guangquan |
| contents | The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial cybersecurity. However, mainstream datasets widely used in academia and industry (e.g., Drebin) exhibit significant limitations: on one hand, their heavy reliance on VirusTotal's multi-engine aggregation results introduces substantial label noise; on the other hand, outdated samples reduce their temporal relevance. Moreover, automated labeling tools (e.g., AVClass2) suffer from suboptimal aggregation strategies, further compounding labeling errors and propagating inaccuracies throughout the research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16835 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research Bai, Hongpeng Dong, Minhong Zhang, Yao Zhao, Shunzhe Zhang, Haobo Li, Lingyue Bai, Yude Xu, Guangquan Cryptography and Security The rapidly evolving Android malware ecosystem demands high-quality, real-time datasets as a foundation for effective detection and defense. With the widespread adoption of mobile devices across industrial systems, they have become a critical yet often overlooked attack surface in industrial cybersecurity. However, mainstream datasets widely used in academia and industry (e.g., Drebin) exhibit significant limitations: on one hand, their heavy reliance on VirusTotal's multi-engine aggregation results introduces substantial label noise; on the other hand, outdated samples reduce their temporal relevance. Moreover, automated labeling tools (e.g., AVClass2) suffer from suboptimal aggregation strategies, further compounding labeling errors and propagating inaccuracies throughout the research community. |
| title | ThreatIntel-Andro: Expert-Verified Benchmarking for Robust Android Malware Research |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2510.16835 |