An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning

TL;DR

This paper introduces an intelligent security architecture for network slicing that employs federated learning with microservices to detect attacks, significantly enhancing security in next-generation networks.

Contribution

It proposes a novel federated learning-based security architecture with microservices for intra-slice and architectural security in network slicing platforms.

Findings

Achieved approximately 95.60% accuracy in architecture-level attack detection.

Achieved 99.99% accuracy in intra-slice attack detection.

Demonstrated the effectiveness of federated learning for scalable, distributed security in network slicing.

Abstract

Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately $95.60\%$ in the network slicing architecture and $99.99\%$ for the deployed slice -- intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.