CryptoCatch: Cryptomining Hidden Nowhere
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910018812510208 |
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| author | Shi, Ruisheng Lin, Ziding Sun, Haoran Wang, Qin Zhang, Shihan Lan, Lina Peng, Zhiyuan Wang, Chenfeng |
| author_facet | Shi, Ruisheng Lin, Ziding Sun, Haoran Wang, Qin Zhang, Shihan Lan, Lina Peng, Zhiyuan Wang, Chenfeng |
| contents | Cryptomining poses significant security risks, yet traditional detection methods like blacklists and Deep Packet Inspection (DPI) are often ineffective against encrypted mining traffic and suffer from high false positive rates. In this paper, we propose a practical encrypted cryptomining traffic detection mechanism. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Our system achieves an F1-score of 0.99 and identifies specific cryptocurrencies with a 99.39\% accuracy rate. Extensive testing across various mining pools confirms the effectiveness of our approach, offering a more precise and reliable solution for identifying cryptomining activities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10573 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CryptoCatch: Cryptomining Hidden Nowhere Shi, Ruisheng Lin, Ziding Sun, Haoran Wang, Qin Zhang, Shihan Lan, Lina Peng, Zhiyuan Wang, Chenfeng Cryptography and Security Cryptomining poses significant security risks, yet traditional detection methods like blacklists and Deep Packet Inspection (DPI) are often ineffective against encrypted mining traffic and suffer from high false positive rates. In this paper, we propose a practical encrypted cryptomining traffic detection mechanism. It consists of a two-stage detection framework, which can effectively provide fine-grained detection results by machine learning and reduce false positives from classifiers through active probing. Our system achieves an F1-score of 0.99 and identifies specific cryptocurrencies with a 99.39\% accuracy rate. Extensive testing across various mining pools confirms the effectiveness of our approach, offering a more precise and reliable solution for identifying cryptomining activities. |
| title | CryptoCatch: Cryptomining Hidden Nowhere |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2602.10573 |