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AI-powered real-time fraud detection across hybrid cloud architectures using stream processing and deep learning
1 Information Technology Manager, 218 TerraStone PL, Cary NC, USA.
2 Director of Engineering, Oregon, USA.
Review
International Journal of Science and Research Archive, 2024, 13(01), 3517-3528.
Article DOI: 10.30574/ijsra.2024.13.1.1978
Publication history:
Received on 06 September 2024; revised on 19 October 2024; accepted on 21 October 2024
Abstract:
Up-to-date types of cyber fraud develop rapidly, making modern fraud detection systems struggle to detect challenging and real-time irregularities. The research evaluates the deployment of artificial intelligence (AI) with stream processing technology and deep learning algorithms for immediate fraud detection that functions in hybrid cloud systems. The combination of public and private cloud infrastructures in hybrid cloud systems provides better scalability and flexibility; however, it generates two security difficulties and data transmission delays. The proposed AI-driven fraud detection system depends on stream processing tools Apache Kafka and Apache Flink combined with LSTM networks and CNNs to determine real-time fraudulent actions. The research design implements data pipeline operations followed by model development and live prediction running inside a hybrid cloud environment to achieve superior speed and high-performance levels. The proposed solution achieves notable performance progress through empirical findings from standard datasets and artificial fraud simulations. A substantial research contribution exists in this work because it delivers a flexible framework for modern hybrid cloud systems which maintain security and scalability features.
Keywords:
Real-Time Fraud Detection; Hybrid Cloud Architecture; Stream Processing; Deep Learning; Artificial Intelligence
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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
