Document Type : Technical Paper


1 Department of Computer Science, Tamale Technical University, Tamale, Ghana.

2 Department of Computer Science, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana.

3 Department of Computer Science, University for Development Studies, Tamale, Ghana.


Hidden Markov Models (HMMs) are machine learning models that has been applied to a range of real-life applications including intrusion detection, pattern recognition, thermodynamics, statistical mechanics among others. A multi-layered HMMs for real-time fraud detection and prevention whilst reducing drastically the number of false positives and negatives is proposed and implemented in this study. The study also focused on reducing the parameter optimization and detection times of the proposed models using a hybrid algorithm comprising the Baum-Welch, Genetic and Particle-Swarm Optimization algorithms. Simulation results revealed that, in terms of Precision, Recall and F1-scores, our proposed model performed better when compared to other approaches proposed in literature.


Main Subjects

[1] L.R.Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition", Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, 1989.
[2] L. Duan, L. Xu, F. Guo, J. Lee, and B. Yan, “A local-density based spatial clustering algorithm with noise”, Information systems, vol. 32, no. 7, pp. 978-986, 2007.
[3] A. Abdallah, A.M. Mohd, and Z. Anazida, "Fraud detection system: A survey" Journal of Network and Computer Applications, Vol. 68, pp.90-113, 2016
[4] A. Srivastava, K. Amlan, S. Shamik and Arun Majumdar, "Credit card fraud detection using hidden Markov model." IEEE Transactions on dependable and secure computing, vol. 5, no. 1, pp. 37-48, 2008.
[5] R. Ahmadian Ramaki, A. Rasoolzadegan, and A. Javan Jafari, "A systematic review on intrusion detection based on the Hidden Markov Model." Statistical Analysis and Data Mining: The ASA Data Science Journal, vol. 11, no. 3, pp. 111-134, 2018.
[6] B. Mor, S. Garhwal, and A. Kumar. "A systematic review of hidden Markov models and their applications." Archives of computational methods in engineering, vol. 28, pp. 1429-1448, 2021.
[7] W. K. Zegeye, R.A. Dean and F. Moazzami, F, “Multi-layer hidden Markov model-based intrusion detection system” Machine Learning and Knowledge Extraction, vol.1 no. 1, pp.265-286, 2018.
[8] X. D Hoang, J. Hu and P. Bertok, “A multi-layer model for anomaly intrusion detection using program sequences of system calls” 11th IEEE International Conference on Networks, 2003. ICON2003, pp. 531-536, 2003.
[9] M. Penagarikano, and B. German "Layered Markov models: A New architectural approach to automatic speech recognition." In Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing pp. 305-314, IEEE, 2004.
[10] O. Abouabdalla, H. El-Taj, A. Manasrah and S. Ramadass, “False positive reduction in intrusion detection system: A survey” In 2009 2nd IEEE International Conference on Broadband Network & Multimedia Technology, pp. 463-466, IEEE, 2009
[11] G.P. Spathoulas and S.K. Katsikas, “Reducing false positives in intrusion detection systems.” computers & security, vol.29, no. 1, pp. 35-44, 2010.
[12] A. Prakash and C. Chandrasekar, "A novel hidden Markov model for credit card fraud detection." International Journal of Computer Applications, vol. 59, no. 3, pp.35-41, 2012.
[13] D.A. Burgio, “Reduction of False Positives in Intrusion Detection Based on Extreme Learning Machine with Situation Awareness”, PhD diss., Nova Southeastern University, 2020.
[14] F.K. Alarfaj, I. Malik, H.U. Khan, N. Almusallam, M. Ramzan and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms” IEEE Access, vol.10, pp. 39700-39715, 2022.
[15] V. Baghdasaryan, H. Davtyan, A. Sarikyan and Z. Navasardyan,” Improving tax audit efficiency using machine learning: The role of taxpayer’s network data in fraud detection” Applied Artificial Intelligence, vol. 36, no. 1, pp. 2012002, 2022.
[16] M. Valavan, and S. Rita. "Predictive-Analysis-based Machine Learning Model for Fraud Detection with Boosting Classifiers." Computer Systems Science & Engineering, vol. 45, no. 1, pp. 232-245, 2023.
[17] N.Q. Do, A. Selamat, O. Krejcar, E. Herrera-Viedma and H. Fujita, “Deep learning for phishing detection: Taxonomy, current challenges and future directions”. IEEE Access, vol.10, pp. 36429-36463, 2022.
[18] H. Fujita, "Effectiveness of a hybrid deep learning model integrated with a hybrid parameterization model in decision-making analysis." In Knowledge innovation through intelligent software methodologies, tools and techniques: proceedings of the 19th international conference on new trends in intelligent software methodologies, tools and techniques (SoMeT_20), vol. 327. 2020.
[19] E. Lopez-Rojas, A. Elmir and S. Axelsson, “PaySim: A financial mobile money simulator for fraud detection”. In 28th European Modeling and Simulation Symposium, EMSS, Larnaca, pp. 249-255. Dime University of Genoa, 2016.
[20] A. dedoyin, “Predicting fraud in mobile money transfer (Doctoral dissertation, University of Brighton), 2018.
[21] A.AA. Danaa, M.I. Daabo and A. Abdul-Barik, “An Improved Hybrid Algorithm for Optimizing the Parameters of Hidden Markov Models” Asian Journal of Research in Computer Science, vol.10, no. 1, pp. 63-73, 2021.
[22] R. Wedge, J.M. Kanter, K. Veeramachaneni, S.M. Rubio and S.I Perez, “Solving the false positives problem in fraud prediction using automated feature engineering in Machine Learning and Knowledge Discovery in Databases” European Conference, ECML PKDD 2018, Dublin, Ireland, pp. 372-388, 2019.