Machine Learning Protocols for Enhanced Cloud Network Security
Poonam Khatarkar, Devesh Pratap Singh, Anil Kumar Sharma · 2023
In fortifying the security of an organization's cloud environment, it is crucial to align security strategies with specific objectives and threats unique to the cloud landscape. This paper proposes an Integrated Augmented Intelligence approach, amalgamating Artificial Intelligence (AI) technologies such as machine learning, natural language processing, and behavioral analytics. The methodology involves thorough evaluation of existing security protocols, identification of AI components based on organizational needs, and integration of cloud-native security tools. Data from diverse cloud sources are processed, and features are extracted for machine learning models. The proposed system includes three key algorithms: Threat Detection using Machine Learning, Real-time Incident Response, and Explainable AI for Cloud Security. The Threat Detection algorithm employs machine learning for real-time threat identification. The Real-time Incident Response algorithm automates responses based on threat severity, significantly reducing response time compared to traditional manual methods. Lastly, the Explainable AI algorithm enhances interpretability, crucial for ensuring human trust in AI-driven security decisions. A comprehensive comparative analysis is performed against six traditional methods, revealing the proposed system's superiority in threat detection accuracy, rapid incident response, scalability, interpretability, and resource efficiency. The integration of augmented intelligence offers continuous improvement, learning from emerging threats.