Cloud-Based IoT Security Management: A Machine Learning Framework for Robust Protection of Heterogeneous Device Networks

The Internet of Things encompasses billions of heterogeneous devices with varying computational capabilities, communication protocols, and security requirements. Managing security across these diverse device populations presents challenges that traditional security approaches cannot address effectively. This paper presents a comprehensive machine learning framework for robust security management in cloud-based IoT systems. The framework integrates device fingerprinting, behavioral anomaly detection, adaptive authentication, and automated threat response, all orchestrated through a cloud-based security management platform. We propose novel device fingerprinting techniques that combine network traffic characteristics with hardware-intrinsic features to reliably identify and authenticate IoT devices. The behavioral anomaly detection component employs ensemble machine learning models that adapt to device-specific behavioral patterns. Experimental evaluation on an IoT testbed with 500 devices across 15 device types demonstrates detection accuracy of 96.2% with a false positive rate of 1.8%.