Digital Identity Protection Using Deep Learning Across Distributed Cloud Security Systems

Abstract

Digital identity has become a critical security asset in distributed cloud environments, where users, devices, applications, APIs, and services continuously exchange sensitive information across heterogeneous infrastructure. Traditional identity protection mechanisms often depend on static authentication rules, predefined access policies, and signature-based threat detection, making them less effective against sophisticated identity theft, credential abuse, account takeover, and anomalous access behavior. This paper proposes a deep learning-based framework for digital identity protection across distributed cloud security systems. The proposed approach integrates behavioral identity analytics, contextual authentication, anomaly detection, risk-based access control, and continuous monitoring into a unified security architecture. Deep learning models analyze authentication events, session characteristics, device attributes, access patterns, network behavior, and contextual security signals to identify deviations from legitimate identity behavior. The framework incorporates distributed data processing to support scalable security monitoring across multi-cloud and geographically dispersed environments while reducing dependence on centralized identity analytics. Privacy-aware data processing, adaptive risk scoring, and automated response mechanisms are incorporated to strengthen resilience against evolving identity-based attacks. The research methodology includes data preprocessing, feature engineering, model development, training, validation, performance evaluation, and comparative analysis. The proposed framework is intended to improve identity anomaly detection, reduce false authentication decisions, enhance continuous identity assurance, and support secure cloud-based digital transformation.