This study proposes a cloud-enabled biometric authentication protocol designed for resource-constrained IoT devices. The system ensures strong identity validation using encrypted biometric traits while preserving speed and minimizing processing demands on embedded sensors and platforms.

Isabelle Fiona Hargreaves, Thomas Wesley Carrington, Henry Mitchell Osborne, Olivia Charlotte Fielding, Dominic Lucas Prescott

This paper introduces a scalable malware detection model using deep learning classifiers optimized for hybrid cloud setups. The model provides high detection accuracy, low false positive rates, and real-time response to rapidly evolving cyber threats in distributed cloud infrastructures.

Lucas Andrew Fenwick, Madeleine Sophie Redman, Zachary Edward Monroe, Francesca Louise Browning, Ethan Samuel Holcombe

This paper presents a federated learning-based model to support confidential cybersecurity data sharing across cloud vendors. It enables collaborative threat detection while retaining data sovereignty and user privacy through encrypted aggregation and secure multiparty computation frameworks.

Charlotte Hazel Ellington, Matthew Julian Caldwell, Eliza Grace Pennington, Jonathan Miles Rowley, Abigail Frances Marsh

This research proposes a cloud-native anomaly detection framework utilizing streaming log analytics. The system continuously monitors operational logs across distributed instances, identifying threats and failures using ensemble learning techniques with real-time visualization dashboards.

Oscar William Delaney, Harriet Amelia Griffiths, Joshua Peter Whitman, Sophie Rebecca Langley, Daniel Edward Rainsford

This paper introduces a novel multi-factor authentication protocol combining behavioral biometrics and token-based access control for secure cloud-based financial services. The model resists spoofing attacks, enhances fraud detection, and maintains user experience through passive continuous identity verification.

Emily Katherine Brewster, Harrison Joseph Stokes, Isabella Faith Harcourt, William Stanley Arkwright, Chloe Isabelle Waddington

This research introduces a blockchain-enhanced access control model that dynamically manages user privileges across distributed cloud systems. Role-aware policy enforcement ensures accountability, data integrity, and verifiable logs while preventing unauthorized escalation of credentials within decentralized application environments.

Leo Frederick Hammond, Natalie Paige Cuthbert, Benjamin Thomas Ormsby, Amelia Joy Kingswell, George Leonard Milburn

This study proposes a cloud-wide encryption framework that rotates cryptographic keys based on predicted access latency and system load. The model improves data confidentiality while minimizing service delays, suitable for large-scale storage solutions with diverse access patterns.

Grace Eleanor Farnsworth, Hugo Oliver Thornton, Daisy Annabel Vickers, Isaac Samuel Donnelly, Matilda Florence Wrenford