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- W4221040755 abstract "Online financial systems like electronic funds transfers (EFT) are heavily dependent on the public Internet. A diverse set of Internet traffic is generated from the use of different online financial platforms. The analysis of raw Internet traffic fails to identify any anomalous behavior in financial transactions. A similar result is obtained when we apply application layer anomaly detection in financial transactions. In this article, we propose a machine learning-based multi-layer framework which will detect and classify anomalous financial transactions. The proposed framework can help a financial service provider to avoid incidents like intrusions and online frauds. It also provides a secure mechanism to detect network anomalies in financial transactions to augment the credibility of such online financial platforms or gateways." @default.
- W4221040755 created "2022-04-03" @default.
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- W4221040755 date "2022-03-28" @default.
- W4221040755 modified "2023-09-23" @default.
- W4221040755 title "Empirical Analysis of Machine Learning Algorithms on Detection of Fraudulent Electronic Fund Transfer Transactions" @default.
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- W4221040755 doi "https://doi.org/10.1080/03772063.2022.2048700" @default.
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