"Secure cloud: A Machine Learning-Enhanced Security Architecture For Cloud Computing With Blockchain Tracing"

Abstract

Cloud computing has emerged as a ubiquitous paradigm for storing, processing, and accessing data, offering unprecedented scalability and flexibility to organizations and individuals. However, the widespread adoption of cloud services has also raised significant security concerns, as traditional security measures often struggle to keep pace with the dynamic and distributed nature of cloud environments. In this paper, we propose "SecureCloud," a novel security architecture for cloud computing that harnesses the synergistic benefits of blockchain technology and machine learning to enhance security and protect against a wide range of threats. SecureCloud integrates blockchain tracing to provide a transparent and tamper-resistant ledger for recording and verifying transactions and data exchanges in the cloud. Additionally, it employs machine learning-based anomaly detection to continuously analyze and adapt to evolving security threats in real-time. Through a comprehensive evaluation and case studies, we demonstrate the effectiveness and robustness of the SecureCloud architecture in addressing key security challenges such as data privacy, integrity, and availability in cloud environments. Our findings underscore the potential of SecureCloud to serve as a foundational framework for enhancing security in cloud computing and safeguarding sensitive data against emerging cyber threats.