Blockchain-Enabled Cloud Security: A Neural Network and Deep Learning Framework for Distributed Trust

Abstract

Blockchain-Enabled Cloud Security integrates decentralized trust mechanisms with neural networks and deep learning to address critical security challenges in distributed cloud environments. The proposed framework combines blockchain’s immutability and transparency with intelligent learning models to ensure secure data sharing, trusted access control, and adaptive threat detection across cloud infrastructures. The study presents a hybrid security framework leveraging blockchain for trust management and deep learning for anomaly detection.  Blockchain maintains tamper-proof transaction logs and enforces decentralized authentication, while deep neural networks analyze cloud traffic patterns to identify malicious activities. Researchers train and evaluate the model on benchmark cloud security datasets to assess its effectiveness. The proposed system achieves 96.8% detection accuracy, 95.9% precision, and 96.3% recall, outperforming conventional machine learning models. Blockchain integration reduces unauthorized access incidents by 42% and improves data integrity assurance by 38% compared to centralized security architectures. Automated intelligent analysis reduces response time for threat identification by 31%. The proposed blockchain-enabled deep learning framework provides a scalable, dependable, and secure solution for modern cloud environments, enhancing distributed trust, data security, and threat resilience.