This paper presents a federated learning-based intrusion detection system designed for multi-tier cloud environments. It preserves data privacy while enabling real-time anomaly detection, improving threat mitigation and enhancing cloud security posture without centralized data aggregation.

Vincent Hugo Ashford, Zoe Madeline Prescott, Gabriel Charles Dalton, Isabel Fiona Langston, Noah Patrick Whitmore

We propose a data synchronization mechanism that protects cloud-edge systems against ransomware threats. The approach ensures continuous replication and secure rollback mechanisms, enabling fast recovery and safeguarding enterprise continuity in the event of malicious encryption attempts.

Henry Tobias Wynn, Olivia Harriet Garrison, Jack Nathaniel Clements, Eliza Margot Bainbridge, Frederick Colin Yates

This study presents a decentralized identity verification architecture using blockchain for enhancing trust and security in cloud ecosystems. It ensures verifiable credentials, access transparency, and revocation control without relying on centralized authorities or third-party providers.

Julian Edward Lockhart, Clara Elise Timmons, Owen Gregory Benson, Natalie Ruth Sanderson, Felix Jordan Caldwell

This paper introduces dynamic load balancing strategies aimed at reducing latency in smart grid communications deployed via cloud infrastructure. The model enhances network performance and data security, ensuring real-time responsiveness in critical grid control systems.

Benjamin Isaac Harrow, Amelia Sophie Thorne, Julian Marcus Renner, Lillian Ava Crowley, George Lucas Armstrong

This study evaluates integrating Zero Trust principles with Secure Access Service Edge (SASE) models for enhancing cloud workload security. The proposed framework minimizes attack surfaces, enforces least privilege access, and dynamically adapts to evolving enterprise threats.

Charlotte Grace Redford, Samuel Louis Durant, Elliot Jacob Hargrove, Victoria Elise Summers, Noah Franklin Pritchard

The paper presents a scalable homomorphic encryption framework that enables privacy-preserving computations in multi-cloud setups. It reduces computational overhead while supporting collaborative analytics on encrypted datasets without compromising data confidentiality or service performance.

Edward Lionel Carrington, Hannah June Westbrook, Sebastian Oliver Quinn, Rebecca Anne Stratton, Leo Mitchell Bowden