An Adaptive Framework Integrating ML Blockchain and TEE for Cloud Security

Cloud computing’s widespread adoption has introduced significant security challenges, particularly in multi-tenant environments where traditional security measures are often inadequate against dynamic threats. This study addresses these vulnerabilities by proposing a novel adaptive security framework for cloud applications. The research objective is to create a robust, multi-layered defense mechanism by synergistically integrating Machine Learning (ML), Blockchain, and Trusted Execution Environments (TEE). Drawing upon a systematic literature review, this paper outlines a conceptual framework where each technology serves a distinct security function. Machine learning provides real-time, predictive threat detection to combat evolving threats like ransomware and zero-day exploits. Blockchain ensures data integrity and transparent, immutable audit trails, mitigating manipulation and unauthorized access. TEE offers hardware-based isolation, creating a secure enclave for processing sensitive data and protecting against system-level exploits. The key finding is that the integration of these technologies provides a holistic and proactive security posture that surpasses siloed approaches. This framework offers a pathway to developing more resilient, efficient, and trustworthy cloud systems, establishing a critical foundation for future research in secure software engineering and cloud infrastructure.