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

Temitayo O. Oyegoke, Kehinde K. Akomolede, Adesola G. Aderounmu and Emmanuel R. Adagunodo
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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

European Journal of Information Technologies and Computer Science, 2021, vol. 1, issue 5, 16-22

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.

Keywords: Spam mail, machine learning, advance fee fraud; fraud detection, neural network (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:epw:comput:v:1:y:2021:i:5:id:10024

DOI: 10.24018/compute.2021.1.5.24

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