Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning
Securing online financial transactions has become a critical concern in an era where financial 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 t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/5/1/10 |
| _version_ | 1797242324475969536 |
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| author | AlsharifHasan Mohamad Aburbeian Manuel Fernández-Veiga |
| author_facet | AlsharifHasan Mohamad Aburbeian Manuel Fernández-Veiga |
| author_sort | AlsharifHasan Mohamad Aburbeian |
| collection | DOAJ |
| description | Securing online financial transactions has become a critical concern in an era where financial 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 financial transactions. Our methodology is based on using two layers of security. The first 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 classifiers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classifier 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, efficacy, and the dynamic nature of various e-commerce platform features. With the evolving financial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work. |
| first_indexed | 2024-04-24T18:37:25Z |
| format | Article |
| id | doaj.art-1a03985b6b2542aebe30e7d9d79ee454 |
| institution | Directory Open Access Journal |
| issn | 2673-2688 |
| language | English |
| last_indexed | 2024-04-24T18:37:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj.art-1a03985b6b2542aebe30e7d9d79ee4542024-03-27T13:17:10ZengMDPI AGAI2673-26882024-01-015117719410.3390/ai5010010Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine LearningAlsharifHasan Mohamad Aburbeian0Manuel Fernández-Veiga1Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah P600, PalestineAtlanTTic Research Center, Universidade de Vigo, 36310 Vigo, SpainSecuring online financial transactions has become a critical concern in an era where financial 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 financial transactions. Our methodology is based on using two layers of security. The first 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 classifiers were tested: logistic regression, decision trees, random forest, and naive Bayes. The results showed that the accuracy of each classifier 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, efficacy, and the dynamic nature of various e-commerce platform features. With the evolving financial landscape, a continuous exploration of authentication factors and datasets to enhance and adapt security measures will be considered in future work.https://www.mdpi.com/2673-2688/5/1/10multi-factor authenticationfraud detectionmachine learningface recognitionuser-friendly system |
| spellingShingle | AlsharifHasan Mohamad Aburbeian Manuel Fernández-Veiga Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning AI multi-factor authentication fraud detection machine learning face recognition user-friendly system |
| title | Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning |
| title_full | Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning |
| title_fullStr | Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning |
| title_full_unstemmed | Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning |
| title_short | Secure Internet Financial Transactions: A Framework Integrating Multi-Factor Authentication and Machine Learning |
| title_sort | secure internet financial transactions a framework integrating multi factor authentication and machine learning |
| topic | multi-factor authentication fraud detection machine learning face recognition user-friendly system |
| url | https://www.mdpi.com/2673-2688/5/1/10 |
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