AI-Driven Network Security for Cloud Systems: Addressing AI Integration Challenges with Multi-Factor Authentication, Multivariate Classification, and Semantic Precedent Retrieval
DOI:
https://doi.org/10.15662/IJEETR.2021.0306005Keywords:
AI-driven network security, Cloud security, Artificial intelligence integration, Multi-factor authentication, Multivariate classification, Semantic Precedent Retrieval, Anomaly detection, Threat intelligence, Identity management, Cloud-native architectures, Cybersecurity automation, Predictive analytics, Real-time threat detection, Security orchestration, Adaptive defense systemsAbstract
The rapid transition to cloud-based infrastructures has intensified the need for intelligent, adaptive, and robust security mechanisms capable of responding to complex cyber threats. This paper proposes an AI-driven network security framework for cloud systems that addresses critical challenges associated with integrating artificial intelligence into security workflows. The architecture employs multi-factor authentication (MFA) to strengthen identity verification and reduce unauthorized access across distributed cloud environments. Multivariate classification models enhance threat detection precision by analyzing multidimensional behavioral, transactional, and network features to identify anomalous or malicious activity. A novel Semantic Precedent Retrieval component augments decision-making by referencing historical threat patterns, security incidents, and contextual metadata to improve risk scoring and policy enforcement. The fusion of these capabilities creates a unified, adaptive defense layer that supports real-time monitoring, automated remediation, and improved situational awareness. Experimental evaluation demonstrates reduced false positives, enhanced model interpretability, and improved resilience against advanced cloud-native attacks. The proposed framework provides a scalable, intelligent foundation for securing next-generation cloud networks.References
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