Detecting Fraudulent Transactions: Using Stacked Autoencoder Kernel ELM Optimized by the Dandelion Algorithm

• Fatima Zohra El Hlouli ^[1]
  1. [1] University Sidi Mohamed Ben Abdellah, Fes Atlas 30003, Morocco
• Localización: Journal of Theoretical and Applied Electronic Commerce Research, ISSN-e 0718-1876, Vol. 18, Nº. 4, 2023, págs. 2057-2076
• Idioma: inglés
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• Resumen

  • The risk of fraudulent activity has significantly increased with the rise in digital payments.

    To resolve this issue there is a need for reliable real-time fraud detection technologies. This research introduced an innovative method called stacked autoencoder kernel extreme learning machine optimized by the dandelion algorithm (S-AEKELM-DA) to detect fraudulent transactions. The primary objective was to enhance the kernel extreme learning machine (KELM) performance by integrating the dandelion technique into a stacked autoencoder kernel ELM architecture. This study aimed to improve the overall effectiveness of the proposed method in fraud detection by optimizing the regularization parameter (c) and the kernel parameter (_). To evaluate the S-AEKELMDA approach; simulations and experiments were conducted using four credit card datasets. The results demonstrated remarkable performance, with our method achieving high accuracy, recall, precision, and F1-score in real time for detecting fraudulent transactions. These findings highlight the effectiveness and reliability of the suggested approach. By incorporating the dandelion algorithm into the S-AEKELM framework, this research advances fraud detection capabilities, thus ensuring the security of digital transactions.