Integrating Machine Learning for Sustaining Cybersecurity in Digital Banks

Overview

Journal Heliyon

Specialty Social Sciences

Date 2024 Sep 18

PMID 39290262

Authors

Muath Asmar

Alia Tuqan

Affiliations

Soon will be listed here.

Abstract

Cybersecurity continues to be an important concern for financial institutions given the technology's rapid development and increasing adoption of digital services. Effective safety measures must be adopted to safeguard sensitive financial data and protect clients from potential harm due to the rise in cyber threats that target digital organizations. The aim of this study is to investigates how machine learning algorithms are integrated into cyber security measures in the context of digital banking and its benefits and drawbacks. We initially provide a general overview of digital banks and the particular security concerns that differentiate them from conventional banks. Then, we explore the value of machine learning in strengthening cybersecurity defenses. We revealed that insider threats, distributed denial of service (DDoS) assaults, ransomware, phishing attacks, and social engineering are main cyberthreats that are digital banks exposed. We identify the appropriate machine learning algorithms such as support vector machines (SVM), recurrent neural networks (RNN), hidden markov models (HMM), and local outlier factor (LOF) that are used for detection and prevention cyberthreats. In addition, we provide a model that considers ethical concerns while constructing a cybersecurity framework to address potential vulnerabilities in digital banking systems. The advantages and disadvantages of incorporating machine learning into the cybersecurity strategy of digital banks are outlined using strengths, weaknesses, opportunities, threats (SWOT) analysis. This study seeks to provide a thorough knowledge of how machine learning may strengthen cybersecurity procedures, protect digital banks, and maintain customer trust in the ecosystem of digital banking.

References

Nguyen T, Nguyen N, Nahavandi S . Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications. IEEE Trans Cybern. 2020; 50(9):3826-3839. DOI: 10.1109/TCYB.2020.2977374. View

Alsowail R, Al-Shehari T . Techniques and countermeasures for preventing insider threats. PeerJ Comput Sci. 2022; 8:e938. PMC: 9044369. DOI: 10.7717/peerj-cs.938. View

Cevikalp H . Best Fitting Hyperplanes for Classification. IEEE Trans Pattern Anal Mach Intell. 2016; 39(6):1076-1088. DOI: 10.1109/TPAMI.2016.2587647. View

Palomares I, Martinez-Camara E, Montes R, Garcia-Moral P, Chiachio M, Chiachio J . A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Appl Intell (Dordr). 2021; 51(9):6497-6527. PMC: 8192224. DOI: 10.1007/s10489-021-02264-y. View

Pattnaik D, Ray S, Raman R . Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review. Heliyon. 2024; 10(1):e23492. PMC: 10770565. DOI: 10.1016/j.heliyon.2023.e23492. View

Han H, Wang L, Qiao J . Efficient self-organizing multilayer neural network for nonlinear system modeling. Neural Netw. 2013; 43:22-32. DOI: 10.1016/j.neunet.2013.01.015. View

Oakley J, Worley C, Yu L, Brooks R, Ozcelik I, Skjellum A . Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion. Digit Threat. 2023; 4(2). PMC: 10629820. DOI: 10.1145/3491258. View

Jafri J, Mohd Amin S, Abdul Rahman A, Mohd Nor S . A systematic literature review of the role of trust and security on Fintech adoption in banking. Heliyon. 2024; 10(1):e22980. PMC: 10756967. DOI: 10.1016/j.heliyon.2023.e22980. View

Sarker I . Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput Sci. 2021; 2(3):160. PMC: 7983091. DOI: 10.1007/s42979-021-00592-x. View

10.

Choi T, Chan H, Yue X . Recent Development in Big Data Analytics for Business Operations and Risk Management. IEEE Trans Cybern. 2016; 47(1):81-92. DOI: 10.1109/TCYB.2015.2507599. View

11.

Guo Y . A Survey of Machine Learning-Based Zero-Day Attack Detection: Challenges and Future Directions. Comput Commun. 2023; 198. PMC: 9890381. DOI: 10.1016/j.comcom.2022.11.001. View

12.

Bastari A, Eliyana A, Syabarrudin A, Arief Z, Emur A . Digitalization in banking sector: the role of intrinsic motivation. Heliyon. 2021; 6(12):e05801. PMC: 7773585. DOI: 10.1016/j.heliyon.2020.e05801. View

13.

Nguyen P, Henkin R, Chen S, Andrienko N, Andrienko G, Thonnard O . VASABI: Hierarchical User Profiles for Interactive Visual User Behaviour Analytics. IEEE Trans Vis Comput Graph. 2019; 26(1):77-86. DOI: 10.1109/TVCG.2019.2934609. View

14.

Zhang Z, Ong Y, Wang D, Xue B . A Collaborative Multiagent Reinforcement Learning Method Based on Policy Gradient Potential. IEEE Trans Cybern. 2019; 51(2):1015-1027. DOI: 10.1109/TCYB.2019.2932203. View

15.

Mi Alnaser F, Rahi S, Alghizzawi M, Ngah A . Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking. Heliyon. 2023; 9(8):e18930. PMC: 10432696. DOI: 10.1016/j.heliyon.2023.e18930. View

16.

Bilen A, Ozer A . Cyber-attack method and perpetrator prediction using machine learning algorithms. PeerJ Comput Sci. 2021; 7:e475. PMC: 8049120. DOI: 10.7717/peerj-cs.475. View