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Customs Fraud Detection Algorithm Based on Ensemble Learning and Multi-head Attention Mechanism
Cyberspace Security | 更新时间：2026-01-27

    • Customs Fraud Detection Algorithm Based on Ensemble Learning and Multi-head Attention Mechanism

    • “In the field of customs declaration fraud identification, experts have proposed a deep self attention ensemble model, which effectively improves the accuracy and robustness of identification, with ACC and AUC indicators reaching 89.81% and 0.8142, respectively. This model provides an efficient and reliable tool for customs departments to detect potential fraudulent activities.”
    • Software Guide   Vol. 25, Issue 1, Pages: 103-109(2026)

    • DOI：10.11907/rjdk.241833    

      CLC： TP391

    • Received：28 October 2024，

      Published：15 January 2026

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  • YU Zhiwen,SHI Shuicai,WANG Hongjun.Customs Fraud Detection Algorithm Based on Ensemble Learning and Multi-head Attention Mechanism[J].Software Guide,2026,25(01):103-109. DOI： 10.11907/rjdk.241833.

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