AI-Enhanced Cloud Security with ERP Integration: MFA, Multivariate Classification, and Transformer-Based Threat Detection
DOI:
https://doi.org/10.15680/IJCTECE.2021.0406012Keywords:
Cloud security, ERP integration, Artificial intelligence, Multi-factor authentication, Multivariate classification, Transformer models, Threat detection, Network security, Anomaly detection, Decision intelligence, Cybersecurity, Enterprise protection, Real-time monitoring, Machine learning, Adaptive securityAbstract
The growing adoption of cloud computing and ERP systems has increased the complexity and vulnerability of enterprise networks. This paper proposes an AI-enhanced cloud security framework with ERP integration, focusing on multi-factor authentication (MFA), multivariate classification, and transformer-based threat detection. The framework leverages machine learning models to analyze large-scale network data and detect anomalies in real time. Transformer-based architectures enhance decision intelligence by contextualizing and correlating threat patterns across multiple data sources. Integration with ERP systems ensures consistent security policies, centralized monitoring, and seamless protection of critical enterprise resources. Experimental results indicate improved detection accuracy, reduced false positives, and enhanced operational efficiency. This framework provides a scalable, adaptive, and AI-driven solution for modern enterprise cloud security.References
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