A Cloud and Network Integrated Architecture Leveraging AI and LLMs for Secure Web Applications and Financial Fraud Analytics
Abstract
The rapid growth of cloud-based web applications and digital financial services has significantly increased the complexity of security threats and financial fraud. Traditional rule-based security systems and isolated analytics platforms are no longer sufficient to address sophisticated cyberattacks and evolving fraud patterns. This paper proposes a cloud and network integrated architecture leveraging artificial intelligence (AI) and large language models (LLMs) to enhance the security of web applications and enable advanced financial fraud analytics. The architecture combines intelligent extract–transform–load (ETL) pipelines, network-aware monitoring, AI-driven anomaly detection, and LLM-based reasoning to deliver real-time and scalable analytics. By integrating cloud infrastructure with network telemetry and financial transaction data, the proposed solution enables holistic visibility, adaptive threat detection, and explainable fraud insights. Experimental evaluation and use-case analysis demonstrate improved detection accuracy, reduced response time, and enhanced system resilience compared to traditional security and fraud detection approaches.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 6 No. 6 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
12835-12842 |
Published |
December 15, 2023 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Georgios Nikolaos Papadopoulos (2023). A Cloud and Network Integrated Architecture Leveraging AI and LLMs for Secure Web Applications and Financial Fraud Analytics. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 6 No. 6 (2023): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 12835-12842. https://doi.org/10.15662/IJAESIT.2023.0606002 |
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