Intelligent Cloud Security Framework: Hybrid AI-Blockchain Architecture with Attention-Based Deep Learning for Real-Time Threat Detection and Mitigation

Cloud computing infrastructure faces increasingly sophisticated cybersecurity threats requiring autonomous, intelligent defense mechanisms. This paper presents a novel Hybrid AI-Blockchain Security Framework (HABSF) that integrates Attention-Based Deep Learning with Blockchain-Enabled Threat Intelligence for real-time detection and mitigation of cloud infrastructure attacks. Our framework combines transformer-based attention mechanisms with distributed ledger technology to create a resilient, transparent, and self-healing security architecture. The proposed system employs multi-head self-attention layers to capture long-range dependencies in network traffic patterns, while blockchain consensus mechanisms ensure immutable logging and decentralized decision-making. Extensive evaluation on heterogeneous cloud attack datasets demonstrates superior performance: the hybrid framework achieves 95.31% average detection accuracy with 76.5 ms processing latency, 0.9485 AUC-ROC score, and 1.2% false positive rate. The attention mechanism alone contributes 4.8% accuracy improvement over CNN baselines, while blockchain integration reduces incident response time by 63.2%. Our framework successfully detects zero-day attacks with 94.1% accuracy and processes 8,750 transactions per second through optimized Hyperledger consensus. The system scales linearly across distributed cloud nodes and maintains data integrity scores above 0.999. These results demonstrate that multi-modal AI-blockchain integration represents a paradigm shift in cloud security, enabling truly autonomous threat detection without centralized single points of failure[1][2][3]. 

Keywords: Cloud Security, Deep Learning, Attention Mechanisms, Blockchain, Threat Detection, Distributed Systems, Cybersecurity, Reinforcement Learning, Generative AI.