AI-Augmented Network Security and Fraud Detection Framework for Cloud-Based Financial Markets

Abstract

Cloud-based financial markets have transformed global trading, banking, and investment operations by enabling high-frequency transactions, real-time analytics, and scalable infrastructure. However, this transformation has also introduced complex cybersecurity threats and sophisticated fraud mechanisms that traditional rule-based security systems struggle to detect. This paper proposes an AI-augmented network security and fraud detection framework designed specifically for cloud-based financial market infrastructures. The framework integrates machine learning and deep learning techniques with real-time network traffic analysis, behavioral analytics, and transaction monitoring to provide proactive threat detection and fraud prevention. By combining supervised learning for known attack patterns with unsupervised anomaly detection for zero-day threats, the proposed system enhances detection accuracy while reducing false positives. The framework is designed for cloud-native deployment, ensuring scalability, low-latency processing, and compliance with financial regulations. Experimental evaluation using simulated financial market data and network traffic demonstrates significant improvements in fraud detection precision, cyber threat visibility, and response time compared to traditional security models. The study highlights the importance of AI-driven security analytics in safeguarding cloud-based financial markets and provides practical insights into implementing intelligent, adaptive, and resilient security architectures.