A Cloud-Edge Integrated Framework for Scalable, Real-time, and Privacy-conscious Threat Detection in IoT Environments

Abstract

The convergence of cloud computing, edge infrastructure, and the Internet of Things (IoT) has reshaped the digital landscape, offering enhanced capabilities while simultaneously broadening the cyberattack surface. This paper proposes a comprehensive framework that integrates cloud and edge resources to deliver scalable, real-time, and privacy-conscious threat detection. Designed to meet the constraints and challenges of distributed IoT ecosystems, the framework leverages federated learning, stream analytics, and modular micro services to ensure timely threat response and regulatory compliance. Extensive experiments demonstrate the framework's ability to maintain low-latency responses, high detection rates, and strict adherence to privacy norms across heterogeneous deployments.