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Open Access

Peer-reviewed

Research Article

Improving the accuracy of cybersecurity spam email detection using ensemble techniques: A stacking approach Machine learning for spam email detection

Ye Tian ,

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft

* E-mail: tiany@ssfjd.cn

Affiliations Academy of Forensic Science, Shanghai, China, Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China

https://orcid.org/0009-0007-2428-2582

⨯
Xin Dai,

Roles Conceptualization

Affiliation Citicbank Software Development Center, Beijing, China
⨯
Zhijun Li,

Roles Data curation

Affiliations Academy of Forensic Science, Shanghai, China, Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China
⨯
Hong Guo,

Roles Conceptualization

Affiliations Academy of Forensic Science, Shanghai, China, Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China
⨯
Xiao Mao

Roles Data curation

Affiliations Academy of Forensic Science, Shanghai, China, Shanghai Forensic Service Platform, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai, China
⨯

Improving the accuracy of cybersecurity spam email detection using ensemble techniques: A stacking approach Machine learning for spam email detection

Ye Tian,
Xin Dai,
Zhijun Li,
Hong Guo,
Xiao Mao

x

Published: September 3, 2025
https://doi.org/10.1371/journal.pone.0331574

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