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A Multi-Layer Perceptron Model for Classification of E-mail Fraud

Author

Listed:
  • Temitayo O. Oyegoke

    (Obafemi Awolowo University, Ile-Ife, Nigeria)

  • Kehinde K. Akomolede

    (The Federal Polytechnic, Nigeria)

  • Adesola G. Aderounmu

    (Obafemi Awolowo University, Nigeria)

  • Emmanuel R. Adagunodo

    (Obafemi Awolowo University, Nigeria)

Abstract

This study was developed an e-mail classification model to preempt fraudulent activities. The e-mail has such a predominant nature that makes it suitable for adoption by cyber-fraudsters. This research used a combination of two databases: CLAIR fraudulent and Spambase datasets for creating the training and testing dataset. The CLAIR dataset consists of raw e-mails from users’ inbox which were pre-processed into structured form using Natural Language Processing (NLP) techniques. This dataset was then consolidated with the Spambase dataset as a single dataset. The study deployed the Multi-Layer Perceptron (MLP) architecture which used a back-propagation algorithm for training the fraud detection model. The model was simulated using 70% and 80% for training while 30% and 20% of datasets were used for testing respectively. The results of the performance of the models were compared using a number of evaluation metrics. The study concluded that using the MLP, an effective model for fraud detection among e-mail dataset was proposed.

Suggested Citation

Handle: RePEc:epw:comput:v:1:y:2021:i:5:id:10024
DOI: 10.24018/compute.2021.1.5.24
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