Ensemble of Artificial Neural Network and Deep Learning for the Prevention of Real-Time Phishing of Financial Data: A Systematic Literature Review
Abstract
This study systematically reviewed relevant literatures on unsupervised machine learning models that have been used for detecting phishing in real-time. Phishing has caused loss of funds, exposed victims to future attacks and caused loss of revenue for financial product users as well as the financial industry at large. Various methods such as Self-Organizing Map, Generative Adversarial Network, Autoencoder, GMM, DBSCAN among many others were reviewed. Among the various methods for preventing phishing in real-time, ensemble model of deep learning and artificial neural network worked better and is the future of anomaly detection that researchers can even use in novel approach, hence it is suggested that future studies should apply the ensemble model used in this study for spamming. A good way of reducing phishing attack is for organizations to train staff who are users of devices, network and systems on how to detect phishing and defend against it. The players in the financial sector needs to understand that trend of technology has changed, hence, there is need for investment in phishing prevention technology and all other forms of cybercrimes. Key words: Phishing, Social engineering, cybercrime, cybersecurity, cyberspace.
Keywords
PhishingSocial engineeringcybercrimecybersecuritycyberspace.
References
Abass, I. A. M. (2018). Social Engineering Threat and Defense: A Literature Survey. Journal of Information Security, 09(04), 257-264. https://doi.org/10.4236/jis.2018.94018 Abdullahi Alabi Abdulrasheed Hamza Bamidele & Abdulrazaq Bashir Oladimej (2023): Cybercrime in Nigeria: Social Influence Affecting the Prevention and Control. Lafia Journal of Economics and Management Sciences: Volume 8, Issue 1. Abidoye, A. P. and Kabao B. (2022): Hybrid Machine Learning: A Tool to Detect Phishing in Communication Network. International Journal of Advanced Computer Science and Application, vol. 11 No 6 pp. 559-569. Aburrous, M., Hossain, M., Dahal, K., & Tabitha, F. (2010): Associative classification techniques for predicting e-banking phishing websites. Multimedia Computing and Information Technology , 2010 International Conference on, 9-12. Aburumman, O., Salleh, A., Omar, K., & Abadi, M. (2020): The impact of human resource management practices and career satisfaction on employee’s turnover intention. Management Science Letters, 10(3), 641-652. Adebowale M. A., Lwin K. T., and Hossain M. A. (2020). “Intelligent phishing detection scheme using deep learning algorithms,'' J. Enterprise Inf. Manage., to be published. [Online]. Available: https://www.emerald.com/insight/content/doi/10.1108/JEIM-01- 2020-0036/full/html, doi: 10.1108/jeim-01-2020-0036. Adenusi Dauda A., Adekunle A. U., Ekuewa J.B., Ayediran O.R. (2020). Challenges and Way Out of Cyber Security Issues In Nigeria. Villanova Journal of Science, Technology and Management . ISSN: 2672-4995. Ahmad I., Alqarni M. A., Ali Almazroi A., and Tariq A. 2020 ‘‘Experimental evaluation of clickbait detection using machine learning models,’’ Intell. Autom. Soft Comput., vol. 26, no. 4, pp. 1335–1344, 2020. Ahmed Salama Abu Zaiter and Samy S. Abu-Naser. (2023). Web page phishing detection Using Neural Network. International Journal of Engineering and Information Systems Vol. 7 Issue 9, September - 2023, Pages: 1-13 Alsariera, Y.A.; Adeyemo, V.E.; Balogun, A.O.; Alazzawi, A.K. 2020 AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites. IEEE Access, 8, 142532–142542. Anderson, Ross, et al. (2016): Measuring the cost of cybercrime, 11th Workshop on the Economics of Information Security (June 2016), Retrieved from http://weis2016.econinfosec.org/papers/Anderson_WEIS2012.pdf Aggarwal, B.K., Gupta, A., Goyal, D., Gupta, P., Bansal, B., & Barak, D.D. (2022): A review on investigating the role of block-chain in cyber security. Materials Today: Proceedings, 56, 3312-3316. Agarwal M., Pasumarthi D., Biswas S. and Nandi S. (2016): “Machine learning approach for detection of flooding DoS attacks in 802.11 networks and attacker localization,” International Journal of Machine Learning and Cybernetics, vol. 7, no. 6, pp. 1035– 1051, Dec., doi: 10.1007/s13042-014-0309-2. Akazue M. I., Ojugo A. A., Yoro R. E., Malasowe B. O., and Nwankwo O. (2022).“Empirical evidence of phishing menace among undergraduate smartphone users in selected universities in Nigeria,” Indonesian Journal of Electrical Engineering and Computer Science , vol. 28, no. 3, pp. 1756–1765, Dec. 2022, doi: 10.11591/ijeecs.v28.i3.pp1756-1765. Akpan, U. I., & Starkey, A. (2021). Review of classification algorithms with changing inter- class distances. Machine Learning with Applications, 4, 100031. Ajlouni, M.I.A., Hadi, W. and Alwedyan, J. (2013): ‘Detecting Phishing Websites Using Associative Classification’, European Journal of Business and Management, p. 5.Akinyelu A. A. and Aderemi O. A. (2014): 'Classification of phishing email using random forest machine learning technique,' Journal of Applied Mathematics, vol. 2014, no. Special. Alghoul, A.; Al-Ajrami, S.; Al Jarousha, G.; Harb, G.; Abu-Naser, S.S. (2018): Email Classification Using Artificial Neural Network. Int. J. Acad. Eng. Res., 2, 8–14 Aljofey, A., Jiang, Q., Rasool, A., Chen, H., Liu, W., Qu, Q., & Wang, Y. (2022). Aneffective detection approach for phishing websites using URL and HTML features. Scientific Reports, 12(1), 1–19. Almseidin, M.; Alkasassbeh, M.; Alzubi, M.; Al-Sawwa, J. Cyber-Phishing WebsiteDetection Using Fuzzy Rule Interpolation. Cryptography 2022, 6, 24. https://doi.org/10.3390/cryptography6020024Aluko, M. (2021). Emerging Cyber Threats in Sub-Saharan Africa. Cybersecurity and Privacy Journal, 3(4), 150-169. Al-Shalabi Luai, Yahia Hasan Jazyah (2022). Phishing Detection Using Hybrid Algorithm Based on Clustering and Machine Learning IJCDS Journal 1570989057Anand, A., and Patel B. (2012): An overview on intrusion detection system and types of attacks it can detect considering different protocols. International Journal of Advanced Research in Computer Science and Software Engineering 38 (1):94–98. Asanka N. G. A., Steve L.& Beznosov K. (2016) Phishing threat avoidance behaviour: An empirical investigation Ayub Akeem Olalekan & Akor Linus (2022): Trends, Patterns and Consequences of Cybercrime in Nigeria. Gusau International Journal of Management and Social Sciences, Federal University, Gusau, Vol.5 , No. 1, April. Azrour, M. et al. (2022): Machine learning algorithms for efficient water quality prediction, Model. Earth Syst. Environ., 2022, Vol. 8, pp. 2793-2801 Balla, A., Habaebi, M. H., Elsheikh, E. A. A., Islam, M. R., & Suliman, F. M. (2023). The Effect of Dataset Imbalance on the Performance of SCADA Intrusion Detection Systems. Sensors, 23(2), Article 2. https://doi.org/10.3390/s23020758 Balaji K 1 , Iyappan K , Manoj Kumar T , Grace Mary S. 2023. Phishing Website Detection using Machine Learning Rules with Cryptography Technique. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT) Volume 3, Issue 3 Bashir, M. (2020). An Analysis of Phishing Emails and How the Human Vulnerabilities are Exploited (Issue October). Springer International Publishing. https://doi.org/10.1007/978-3-030-52581-1 Beck, U. (1992): Risk society: towards a new modernity. London: Sage publication.Bergholz A., Jan De Beer S. G. and Marie-Francine Moens G. P. S. (2010). 'New filtering approaches for phishing email,' Journal of computer security, vol. vol. 18, pp. 7-35,. Bhanu Y. P, Venkatesulu D. (2024): PDSMV3-DCRNN: A novel ensemble deep learning framework for enhancing phishing detection and URL extraction. Journal of Computers & Security 148 (2025) 104123. Bharadiya, J. P. (2023): A Comprehensive Survey of Deep Learning Techniques Natural Language Processing. European Journal of Technology, 7(1), 58 - 66. Bhavsar V., Kadlak A., and Sharma S. (2021). “Study on phishing attacks,” International Journal of Computer Applications, vol. 182, no. 33, pp. 27-29, 2018.Bhowmick A. and Hazarika S.M. (2016). Machine learning for e-mail spam filtering: review techniques and trends Bisallah H. Ibrahim, Habiba U. Salihu and Yusuf A. Aleshinloye (2025). Rule-Based Approach to e-Commerce Fraud Detection. UNIABUJA Journal of Engineering and Technology. Volume 2, Issue 1, 2025; 196-204Borodkin, K., Nurtas, M., Altaibek, A., Daineko, Y., & Otepov, T. (2023). Data Pre-processing and Visualization for Machine Learning Models and its Applications in Education. 8th International Conference on Digital Technologies in Education, Science and Industry. Bossler, A.& Berenblum, T. (2019): Introduction: new directions in cybercrime research. Journal of Crime and Justice 42(5):495-499. Broadhurst, R., Skinner, K., Sifniotis, N., Matamoros-Macias, B., & Ipsen, Y. (2020). Phishing risks in a university student community. Trends and Issues in Crime and Criminal Justice, 2(587), 4-23. https://doi.org/10.52922/ti04251Bu S.-J. and Cho S.B. (2021), “Deep character-level anomaly detection based on a convolutional autoencoder for zero-day phishing URL detection,''Electronics, vol. 10, no. 12, p. 1492, Jun. 2021, doi: 10.3390/electron-ics10121492. Bullee, J., Montoya, L., Junger, M., & Hartel, P. (2017). Spear phishing in organisations explained. Information & Computer Security, 25(5
More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY
A Flexible Edge Search Algorithm for Graceful Tree Labeling
Author: Michael Arnold and Fabio Vitor
Advances in Algorithmic Contract Scoring for Pre-Negotiation Yield Optimization and Risk Retention
Author: Ngozi Samuel Uzougbo, Michael Ominyi, Cyril Chimelie Anichukwueze, Blessing, Chika Jones
DevTest flow: Designing a Scalable Continuous Testing Pipeline for High-Velocity Software Delivery
Author: Lawal Ahmed Oladimeji, Achori Busayo, Akeju BusayoZainab, Saka Samson, Damilare, Mbah Demian Chidi, Runsewe Similoluwa Mayowa, Oladiti Luqman, Abiodun
Assessing Blackboard LMS Implementation, Adoption Barriers and Digital Transformation in Tertiary Institutions in Kogi State, Nigeria
Author: Okolo Clement, Eluemuno
Intelligent Cybercrime Prediction and Control Model Using Hybrid Machine Learning Algorithms for Ddos and Phishing Attack Mitigation
Author: Chukumeka Gift Iroanwusi, Davies Isobo Nelson