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Patel, R. and Mehta, D. (2023) Federated Learning for Privacy-Preserving Fraud Detection. IEEE Transactions on Neural Networks and Learning Systems, 33, 456-467.

has been cited by the following article:

• TITLE: Securing Consumer Banking Websites Using Machine Learning: A Mathematical and Practical Approach (Working 2024)

  AUTHORS: Fahad Al-Zahrani

  KEYWORDS: Anomaly Detection, Cybersecurity, Distributed Denial of Service (DDoS), Fraud Detection, Machine Learning, Phishing Attacks, Secure Banking Operations

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.3, March 20, 2025

  ABSTRACT: Cybersecurity challenges in consumer banking websites have surged, driven by increasingly sophisticated threats such as fraud, phishing, and Distributed Denial of Service (DDoS) attacks. This study introduces an innovative machine learning framework designed to counter these challenges through real-time threat detection and mitigation. The proposed approach integrates advanced techniques such as autoencoders for anomaly detection, logistic regression for fraud classification, and reinforcement learning for DDoS attack prevention. Evaluated on enriched banking datasets, the framework achieved exceptional performance metrics, with high precision, recall, and Area Under the Curve (AUC) scores. This research highlights the transformative role of machine learning in ensuring secure banking operations and outlines future directions, including blockchain integration and federated learning, for enhanced scalability and privacy.