Federated Learning and Quantum Components for Robust Threat Detection in Cloud-Based Systems

Precise threat detection in cloud-based environment is crucial for detecting unauthorized access, security attacks, and vulnerabilities. Several Artificial Intelligence based threat detection techniques have been implemented and they provide better threat detection result but they are struggled with lower detection results, poor scalability, and model complexity. This study introduces an automated Modified vision transformer-based Federated learning network for effective threat detection in cloud environment. The proposed framework incorporates several layer like perception, transport, cloud, and Intelligence layers. The perception layer is responsible for data collection whereas transport layer enables secure data transmission and performs mutual authentication. In the cloud layer, the threat detection is performed where Modified Vision Transformer for deriving spatio-temporal features, which is vital for attack detection. The Federated Learning is applied for contextual analysis, that utilizes Gated Recurrent Units for local model training and Artificial Hummingbird Algorithm for model aggregation. For accurate threat detection, the Hybrid Quantum Generative Adversarial Network is designed. Granular computing is applied in the Intelligence layers to offer context-aware threat actions. Simulation validation on attack datasets indicates that the proposed mechanism attains 98.89% detection accuracy and 98.48% security rate, which is superior than existing works.