AI-Driven Network Security Architecture for Edge Computing

Abstract

Edges pose a fundamental change to network security architecture. Edge computing moves computation, and thus exposure, to many physical locations, making customary enforcement points ineffective. Security architecture must consider the dynamic topology and variable connectivity, as well as the huge attack surface of billions of devices generating large volumes of data on the edge of the network. Distributed intelligence architectures implement a tiered security processing model. Security processing occurs on both edge and core compute, enabling real-time threat detection with low-overhead machine learning inference on the edge and centralized correlation analysis. APIs support programmable, policy-based automation of security across multi-vendor infrastructure, including zero-touch device provisioning and intent-based security operations. More advanced threat detection relies on behavioral baselining, predictive modeling, and ensemble learning to detect threats across multiple threat classes with a high level of precision and recall. Zero-trust architectures replace binary authentication with continual risk assessment and dynamic policy application. Zero-trust networks enforce micro-segmentation to restrict lateral movement and reduce an attack's blast radius. To extend advanced security capabilities to resource-constrained edge devices, model compression and energy-efficient inference at the edge are popular approaches to hardware acceleration. It can also considerably reduce the computational cost while maintaining detection performance with this integrated framework to address the security problems in distributed computing environments through smart automation, adaptive controls, and resource optimization.