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 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.