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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.

References

  1. Behdad M, Barone L, Bennamoun M, French T. Nature-inspired techniques in the context of fraud detection. IEEE Transactions on Systems, Man and Cybernetics – Part C, Applications and Reviews. 2012; 42(6): 1273-1290.
     Google Scholar
  2. Tive C. 419 Scam, Exploits of the Nigerian Con Man: Bloomington, iUniverse; 2006.
     Google Scholar
  3. Reich P. Advance Fee Schemes in Country and Across Borders. Proceeding of Crime in Australia International Connections Conference organized by Australian Institute of Criminology; Melbourne, Australia; 2004.
     Google Scholar
  4. Australian Competition and Consumer Commission (ACCC). Upfront Payment and Advance Fee Frauds [Internet]. 2017. [cited on 2017 November 30]. Available from: https//www.scamwatch.gov.au /types-of-scams/unexpected-money/up-front-payment-advanced-fee-frauds on.
     Google Scholar
  5. Nizamani S, Memon N, Glasdam M, Nguyen DD. Detection of fraudulent emails by employing advance feature abundance. Egyptian Informatics Journal. 2014; 15: 169-174.
     Google Scholar
  6. Fraud MR. Detection using supervised machine learning algorithms. International Journal of Advanced Research in Computer and Communication Engineering. 2017; 6(6): 6-10.
     Google Scholar
  7. Carcillo F, Dal Pozzolo A, Le Borgne Y.-A, Caelen O, Mazzer Y, Bontempi G. SCARFF: a Scalable Framework for Streaming Credit Card Fraud Detection with Spark. [Internet]. 2017. [cited on 2017 September 23]. Available from: https://doi.org/10.1016/j.inffus.2017.09.005.
     Google Scholar
  8. Zhang H, Li D. Naïve Bayes Text Classifier. Proceedings of the IEEE International Conference of Computing. 2007; 708-713.
     Google Scholar
  9. Abu-Nimeh S, Nappa D, Wang X, Nair S. A Comparison of Machine Learning Techniques for Phishing Detection. Proceedings of Anti-Phishing Working Groups 2nd Annual e-Crime Researchers Summit. 2007; 60-69.
     Google Scholar
  10. Amor NB, Benferhat S, Elouedi Z. Naïve Bayes vs decision trees in intrusion detection systems. Proceedings of the 2004 ACM Symposium - Applied Computing. 2004; 420-424.
     Google Scholar
  11. Sculley D, Cormack G. Filtering email spam in the presence of noisy user feedback. Proceedings of the 5th Email Anti-Spam Conference, 2008; 1-10.
     Google Scholar
  12. Chandrasekaran M, Narayanan K, Upadhyaya S. Phishing E-Mail detection based on structural properties. Proceedings of the 1st Annual Symposium on Information Assurance, Intrusion Detection Prevention, 2006; 2-8.
     Google Scholar
  13. Kim DS, Nguyen H.-N, Park JS. Genetic algorithm to improve SVM based network intrusion detection system. Proceedings of the 19th Conference of Advanced Information and Network Applications 2, 2005; 155-158.
     Google Scholar
  14. Degang Y, Guo C, Hui W, Xiaofeng L. Learning vector quantization neural network method for network intrusion detection. University of Wuhan University Journal of Natural Sciences. 2007; 12(1), 147-150.
     Google Scholar
  15. Su M.-Y, Yeh S.-C, Chang, K.-C, Wei H.-F. Using incremental mining to generate fuzzy rules for real-time network intrusion detection systems. Proceedings of the 22nd International Advanced Information and Network Application Conference. 2008; 50-55.
     Google Scholar
  16. Rehak M, Pechoucek P, Celeda P, Krmicek V, Grill M, Bartos K. Multi-agent approach to network intrusion detection. Proceedings of the 7th International Joint Autonomous Agents and Multi-agent Systems Conference. 2008; 1695-1696.
     Google Scholar
  17. Srivastava A, Kundu A, Sural S, Majumdar A. Credit card fraud detection using hidden Markov model. IEEE Transactions on Dependable Secure Computers. 2008; 5(1): 37-48.
     Google Scholar
  18. Sanchez D, Vila M, Cerda L, Serrano J. Association rules applied to credit card fraud detection. Expert Systems Application. 2009; 36(2): 3630-3640.
     Google Scholar
  19. Panigrahi S, Kundu A, Sural S, Majumdar A. Credit card fraud detection, a fusion approach using Dempster–Shafer theory and Bayesian learning. Information Fusion. 2009; 10(4): 354-363.
     Google Scholar
  20. Bolton RJ, Hand DJ. Statistical fraud detection, A review. Statistical Science Journal. 2002;17(3): 235-249.
     Google Scholar
  21. Zavvar M, Razaei M, Garavand S. E-Mail spam detection using a combination of particle swarm optimization and artificial neural network and support vector machine. International Journal of Modern Education and Computer Science. 2016; 7: 68-74.
     Google Scholar
  22. Choudhary M, Dhaka A. Automatic e-mail classification using genetic algorithm. International Journal of Computer Science and Information Technologies. 2015; 6(6): 5097-5103.
     Google Scholar
  23. Bahnsen AC, Stojanovic A, Aouada D, Ottersten, B. Cost sensitive credit card fraud detection using bayes minimum risk. Proceedings of the 12th International Conference on Machine Learning and Applications. 2013; 333-338.
     Google Scholar
  24. Chaudhary K, Yadav J, Mallick B. A review of fraud detection techniques, credit card. International Journal of Computer Applications. 2012; 45(1): 39-44.
     Google Scholar