Cybersecurity Framework for Banking Systems: A Multi-Layer Defense Architecture Using Machine Learning, Microservices, and Zero-Trust Principles
Tata Consultancy Services, Columbus OH, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 24(03), 3629-3638
Publication history:
Received on 23 October 2024; revised on 21 December 2024; accepted on 28 December 2024
Abstract:
The exponential growth of digital banking platforms has created unprecedented cybersecurity challenges including distributed denial-of-service attacks, advanced persistent threats, credential stuffing attacks, API vulnerabilities, and insider threats targeting financial infrastructure. Contemporary banking security systems struggle with fragmented defense mechanisms, inadequate real-time threat detection, insufficient encryption key management, limited audit trails, and poor integration across authentication, authorization, vulnerability management, and disaster recovery components. This research introduces a comprehensive cloud-native intelligent cybersecurity framework implementing twelve integrated security layers encompassing authentication with multi-factor verification, role-based authorization with least privilege enforcement, end-to-end encryption with transport layer security protocols, vulnerability management with continuous monitoring, audit compliance with comprehensive logging, network security with intrusion detection systems, terminal security with device management, emergency response with incident protocols, container security with runtime protection, API security with rate limiting, third-party vendor risk management, and disaster recovery with system redundancy. The framework leverages Java microservices architecture deployed on Kubernetes for horizontal scalability, Angular-based security dashboards for real-time monitoring, machine learning algorithms including random forests and recurrent neural networks for anomaly detection achieving 98.4% threat identification accuracy, and blockchain-enabled immutable audit trails. Experimental validation across simulated banking environments processing 4.2 million daily transactions demonstrates 94% reduction in mean time to detect security incidents, 87% improvement in false positive reduction, 99.97% system availability, and compliance with SOC 2, PCI-DSS, and GDPR regulatory requirements. This research establishes a comprehensive paradigm for financial institution cybersecurity combining cloud infrastructure, intelligent threat detection, and defense-in-depth principles.
Keywords:
Banking cybersecurity; Cloud security; Machine learning threat detection; Microservices architecture; Zero-trust security; API security; Disaster recovery
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0