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Conference

Risk-Adaptive Multi-Factor and Continuous Authentication Framework for Intelligent Identity Verification in Cloud Computing

Aug 2026 · 2026 6th International Conference on Soft Computing for Security Applications (ICSCSA) · pp. 1048-1054 · 0 citations · 21 references

Abstract

Cloud computing environments demand identity verification mechanisms that jointly satisfy strong security and unobtrusive usability, yet conventional multi-factor authentication (MFA) verifies identity only once, at session initiation, leaving a persistent vulnerability window for the remainder of an active session. This paper presents RA-MFA-Cloud, a risk-adaptive multi-factor continuous authentication framework for intelligent identity verification in cloud computing. The framework fuses knowledge-based, possessionbased, and biometric authentication factors with behavioral biometrics profiling inside a dynamic risk-scoring engine. Unlike static MFA schemes, RA-MFA-Cloud continuously monitors user sessions and adaptively adjusts authentication requirements from real-time risk assessments that incorporate contextual, behavioral, and temporal features. The risk model computes a weighted composite score from session anomaly, device trust, geolocation consistency, and behavioral deviation, and a cloud-specific contextual analysis module folds multitenant isolation indicators, service-level access patterns, and resource-consumption anomalies into the same pipeline - a combination absent from prior cloud authentication frameworks. Evaluated on 10,000 cloud sessions collected from a production multi-tenant environment, RA-MFA-Cloud achieves 98.7% authentication accuracy, a 99.2% impostor detection rate (IDR), a 0.3% false acceptance rate (FAR), and 23 ms average decision latency, outperforming static MFA and single-factor baselines across every metric while adding only 1 ms of latency over static two-factor authentication. Ablation experiments isolate the individual contribution of each framework component, confirming that risk-adaptive scoring and behavioral biometrics are the two largest contributors to overall accuracy. These results indicate that RA-MFA-Cloud offers a practical, deployable path toward continuous, risk-aware identity assurance for multi-tenant cloud platforms.

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