Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection
Online banking fraud has become increasingly prevalent with the widespread adoption of digital financial services, necessitating advanced security solutions capable of detecting both known and emerging threats. This paper presents a robust machine learning framework that integrates anomaly detection...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Informatics Department, Faculty of Computer Science Bina Darma University
2025-06-01
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| Series: | Journal of Information Systems and Informatics |
| Subjects: | |
| Online Access: | https://journal-isi.org/index.php/isi/article/view/1076 |
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| _version_ | 1849240651345952768 |
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| author | Sheunesu Makura Caden Dobson Seani Rananga |
| author_facet | Sheunesu Makura Caden Dobson Seani Rananga |
| author_sort | Sheunesu Makura |
| collection | DOAJ |
| description | Online banking fraud has become increasingly prevalent with the widespread adoption of digital financial services, necessitating advanced security solutions capable of detecting both known and emerging threats. This paper presents a robust machine learning framework that integrates anomaly detection with network packet analysis to mitigate fraudulent activities, focusing particularly on Distributed Denial of Service (DDoS) attacks. The key contribution is an ensemble model combining Isolation Forest and K-means clustering, which achieves 98% accuracy and 98% F1-score in anomaly detection while reducing false positives to 2% which is a critical improvement for operational deployment in banking systems. The framework’s semi-supervised architecture enables zero-day fraud detection without reliance on labeled attack data, addressing a fundamental limitation of signature-based systems. By leveraging feature optimization (PCA/t-SNE) and real-time processing capabilities, this solution offers financial institutions a practical, adaptive defense mechanism against evolving cyber threats. The results demonstrate significant potential for integration into existing banking security infrastructures to enhance fraud prevention with minimal disruption. |
| format | Article |
| id | doaj-art-94ec9c1b09fa4e6fa3a195388a6dc86b |
| institution | Kabale University |
| issn | 2656-5935 2656-4882 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Informatics Department, Faculty of Computer Science Bina Darma University |
| record_format | Article |
| series | Journal of Information Systems and Informatics |
| spelling | doaj-art-94ec9c1b09fa4e6fa3a195388a6dc86b2025-08-20T04:00:28ZengInformatics Department, Faculty of Computer Science Bina Darma UniversityJournal of Information Systems and Informatics2656-59352656-48822025-06-01721153118310.51519/journalisi.v7i2.10761076Mitigating Online Banking Fraud Using Machine Learning and Anomaly DetectionSheunesu Makura0Caden Dobson1Seani Rananga2University of PretoriaUniversity of PretoriaUniversity of PretoriaOnline banking fraud has become increasingly prevalent with the widespread adoption of digital financial services, necessitating advanced security solutions capable of detecting both known and emerging threats. This paper presents a robust machine learning framework that integrates anomaly detection with network packet analysis to mitigate fraudulent activities, focusing particularly on Distributed Denial of Service (DDoS) attacks. The key contribution is an ensemble model combining Isolation Forest and K-means clustering, which achieves 98% accuracy and 98% F1-score in anomaly detection while reducing false positives to 2% which is a critical improvement for operational deployment in banking systems. The framework’s semi-supervised architecture enables zero-day fraud detection without reliance on labeled attack data, addressing a fundamental limitation of signature-based systems. By leveraging feature optimization (PCA/t-SNE) and real-time processing capabilities, this solution offers financial institutions a practical, adaptive defense mechanism against evolving cyber threats. The results demonstrate significant potential for integration into existing banking security infrastructures to enhance fraud prevention with minimal disruption.https://journal-isi.org/index.php/isi/article/view/1076machine learningfraud mitigationonline bankinganomaly detectionnetwork packetsfraud detection |
| spellingShingle | Sheunesu Makura Caden Dobson Seani Rananga Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection Journal of Information Systems and Informatics machine learning fraud mitigation online banking anomaly detection network packets fraud detection |
| title | Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection |
| title_full | Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection |
| title_fullStr | Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection |
| title_full_unstemmed | Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection |
| title_short | Mitigating Online Banking Fraud Using Machine Learning and Anomaly Detection |
| title_sort | mitigating online banking fraud using machine learning and anomaly detection |
| topic | machine learning fraud mitigation online banking anomaly detection network packets fraud detection |
| url | https://journal-isi.org/index.php/isi/article/view/1076 |
| work_keys_str_mv | AT sheunesumakura mitigatingonlinebankingfraudusingmachinelearningandanomalydetection AT cadendobson mitigatingonlinebankingfraudusingmachinelearningandanomalydetection AT seanirananga mitigatingonlinebankingfraudusingmachinelearningandanomalydetection |