Multi-Cloud Identity Verification Frameworks: AWS/GCP Hybrid Architectures for Real-Time Fraud Mitigation
Abstract
The exponential rise in global online transactions intensified the complexity and frequency of identity-fraud attacks, necessitating distributed and intelligent verification systems. A hybrid multi-cloud identity verification framework was designed and evaluated by combining Amazon Web Services (AWS) and Google Cloud Platform (GCP). The framework integrated event-driven serverless components, cross-cloud streaming analytics, and machine-learning-based anomaly detection. In controlled experiments spanning 500–5000 requests per second, P95 decision latency was reduced by 28–31% relative to single-cloud baselines (290ms vs. 420/405ms), and the false-positive rate was reduced by 33–38% (2.8% vs. 4.5%/4.2%). Overall detection accuracy reached 96.4% and end-to-end system availability reached 99.9%.
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References
- Amazon Web Services, “Amazon cognito documentation,” https://docs.aws.amazon. com/cognito/, 2025, accessed: 2025-08-17.
- Google Cloud, “Identity platform documentation,” https://cloud.google.com/identityplatform/docs, 2025, accessed: 2025-08-17.
- M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. Patterson, A. Rabkin, I. Stoica, and M. Zaharia, “A view of cloud computing,” Communications of the ACM, vol. 53, no. 4, pp. 50–58, 2010.
- J. Kreps, N. Narkhede, and J. Rao, “Kafka: a distributed messaging system for log processing,” in Proceedings of the NetDB Workshop, 2011.
- C. Olston, N. Fiedel, K. Gorovoy, D. Harmsen, F. Lao, V. Rajashekhar, S. Ramesh, J. Soyke, C. Wang, M. Wicke, Y. Xia, and T. Yuen, “Tensorflow-serving: Flexible, high-performance ml serving,” in Proceedings of the Workshop on ML Systems, 2017.
- I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proc. International Conference on Information Systems Security and Privacy (ICISSP), 2018, pp. 108–116.
- D. W. Chadwick and G. Inman, “Attribute aggregation in federated identity management,” Computer, vol. 42, no. 5, pp. 33–40, 2009.
- A. C. Bahnsen, D. Aouada, A. Stojanovic, and B. Ottersten, “Cost sensitive credit card fraud detection using bayes minimum risk,” in Proc. IEEE International Conference on Machine Learning and Applications (ICMLA), 2013, pp. 333–338.
- A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, “Credit card fraud detection: A realistic modeling and a novel learning strategy,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 8, pp. 3784–3797, 2018.
- S. Rose, O. Borchert, S. Mitchell, and S. Connelly, “Zero trust architecture,” NIST Special Publication, vol. 800-207, 2020.