[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118104-en":3,"doc-seo-118104-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118104,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Secure Internet Financial Transactions - A Framework Integrating Multi-Factor Authentication and Machine Learning","Securing online financial transactions is a critical challenge as financial services continue to digitize daily payment flows. This study proposes a two-layer framework combining multi-factor authentication with machine learning to reduce exposure to cybercriminal fraud. The authentication layer uses multiple factors to verify users, while the ML layer activates upon detection of suspicious activity and applies facial recognition for additional protection. Four supervised classifiers are evaluated, achieving accuracies of 97.938%, 97.881%, 96.717%, and 92.354% and supporting usability and efficacy goals.","Article  \nSecure Internet Financial Transactions: A Framework  \nIntegrating Multi-Factor Authentication and Machine Learning  \nAlsharifHasan Mohamad Aburbeian 1, * and Manuel Fern¡ndez-Veiga 2  \n1 Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah P600, Palestine  \n2 AtlanTTic Research Center, Universidade de Vigo, 36310 Vigo, Spain; mveiga@det.uvigo.es  \n* Correspondence: [a.aburbeian@student.aaup.edu](a.aburbeian@student.aaup.edu)  \nAbstract: Securing online 􀀂nancial transactions has become a critical concern in an era where 􀀂nancial services are becoming more and more digital. The transition to digital platforms for conducting daily transactions exposed customers to possible risks from cybercriminals. This study proposed a framework that combines multi-factor authentication and machine learning to increase the safety of online 􀀂nancial transactions. Our methodology is based on using two layers of security. The 􀀂rst layer incorporates two factors to authenticate users. The second layer utilizes a machine learning component, which is triggered when the system detects a potential fraud. This machine learning layer employs facial recognition as a decisive authentication factor for further protection. To build the machine learning model, four supervised classi􀀂ers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classi􀀂er was 97.938%, 97.881%, 96.717%, and 92.354%, respectively. This study's superiority is due to its methodology, which integrates machine learning as an embedded layer in a multi-factor authentication framework to address usability, ef􀀂cacy, and the dynamic nature of various e-commerce platform features. With the evolving 􀀂nancial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work.  \nCitation: Aburbeian, A.M.; Fernández-Veiga, M. Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning. AI 2024, 5, 177􀂖194 . [https://doi.org/10.3390/ai5010010](https://doi.org/10.3390/ai5010010)  \nAcademic Editor: Ke-Lin Du  \nReceived: 8 December 2023  \nRevised: 5 January 2024  \nAccepted: 7 January 2024  \nPublished: 10 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nKeywords: multi-factor authentication; fraud detection; machine learning; face recognition; user-friendly system  \n1. Introduction  \nFinTech is described as a new 􀀂nancial development that enhances and automates 􀀂nancial services [1] . Mobile wallets, online banking, and payment gateways that offer quick and easy services are examples of 􀀂nancial technologies [2] . The increasing use of such technologies has led to a rise in fraudulent transactions, which makes securing these transactions an issue [3] . Authentication is a procedure in which a user submits some form of credentials to prove identity [4] . The authentication techniques can be one of three categories: something you know (password), something you have (tokens, cards), and something you are (biometrics) [5] . A password has been widely used as a single-factor authentication technique to secure communication between two entities [6] . Although it is a straightforward and easy-to-implement mechanism, it is not suf􀀂cient because of its high ability to be revealed [7] . Sharing the password immediately compromises the account. Furthermore, unauthorized access can be gained using a rainbow table [8], a dictionary attack [9], or social engineering approaches [10] . Following the demonstration that authentication with one f","cbCaiqSGKBqgT2gb","https://ap.wps.com/l/cbCaiqSGKBqgT2gb","pdf",3107587,1,18,"English","en",105,"# Introduction\n## Authentication and Multi-Factor Authentication (MFA)\n## Machine Learning for Fraud Detection\n# Proposed Secure Framework\n## Multi-Layer Security Design\n## Facial Recognition Triggered by Suspicion\n# Experimental Setup and Classifiers\n## Logistic Regression\n## Decision Trees\n## Random Forest\n## Naive Bayes","[{\"question\":\"What framework does the study propose for securing online financial transactions?\",\"answer\":\"It proposes a two-layer framework that combines multi-factor authentication with a machine learning component for fraud detection and response.\"},{\"question\":\"How does the machine learning layer improve transaction security?\",\"answer\":\"It activates when the system detects potential fraud and uses facial recognition as an additional decisive authentication factor.\"},{\"question\":\"Which supervised classifiers are tested, and what accuracy ranges are reported?\",\"answer\":\"The study evaluates logistic regression, decision trees, random forest, and naive Bayes, reporting accuracies of 97.938%, 97.881%, 96.717%, and 92.354% respectively.\"}]","Secure Internet Financial Transactions - 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