Cyber-Resilient AI Framework for Financial Cloud Risk Analytics: Multi-Modal Deep Learning with WSN and KNN Optimization
DOI:
https://doi.org/10.15662/IJRAI.2025.0806006Keywords:
AI-Powered Cloud Computing, Cyber Resilience, Financial Risk Analytics, Deep Learning, WSN, KNN, Predictive Security, Fraud DetectionAbstract
The growing integration of financial services with cloud technologies has introduced new challenges in risk assessment, cybersecurity, and data integrity. This paper proposes a Cyber-Resilient AI Framework designed to enhance Financial Cloud Risk Analytics through the fusion of Multi-Modal Deep Learning (MMDL), Wireless Sensor Networks (WSN), and K-Nearest Neighbor (KNN) optimization. The framework leverages multi-source data streams from cloud-based financial ecosystems and WSN-enabled IoT infrastructures to provide real-time situational awareness and anomaly detection. Deep learning models extract latent features across structured and unstructured data, while the KNN algorithm optimizes classification accuracy and adaptive threat prediction. Cyber resilience is achieved through dynamic intrusion detection, continuous learning loops, and intelligent decision support for financial risk governance. Experimental validation demonstrates improved precision in identifying fraudulent activities, enhanced data confidentiality, and reduced false-positive rates in cloud-enabled financial environments. The proposed model provides a scalable, intelligent, and secure foundation for next-generation financial analytics platforms.
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