Open Access

ARTICLE

Federated Network Intelligence Orchestration for Scalable and Automated FL-Based Anomaly Detection in B5G Networks

Pablo Fernández Saura^1,*, José M. Bernabé Murcia¹, Emilio García de la Calera Molina¹, Alejandro Molina Zarca², Jorge Bernal Bernabé¹, Antonio F. Skarmeta Gómez¹

1 Department of Information and Communications Engineering, University of Murcia, Murcia, 30100, Spain
2 University Center of Defense, Spanish Air Force Academy, San Javier, 30720, Spain

* Corresponding Author: Pablo Fernández Saura. Email:

(This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)

Computers, Materials & Continua 2024, 80(1), 163-193. https://doi.org/10.32604/cmc.2024.051307

Received 01 March 2024; Accepted 20 May 2024; Issue published 18 July 2024

Abstract

The management of network intelligence in Beyond 5G (B5G) networks encompasses the complex challenges of scalability, dynamicity, interoperability, privacy, and security. These are essential steps towards achieving the realization of truly ubiquitous Artificial Intelligence (AI)-based analytics, empowering seamless integration across the entire Continuum (Edge, Fog, Core, Cloud). This paper introduces a Federated Network Intelligence Orchestration approach aimed at scalable and automated Federated Learning (FL)-based anomaly detection in B5G networks. By leveraging a horizontal Federated learning approach based on the FedAvg aggregation algorithm, which employs a deep autoencoder model trained on non-anomalous traffic samples to recognize normal behavior, the system orchestrates network intelligence to detect and prevent cyber-attacks. Integrated into a B5G Zero-touch Service Management (ZSM) aligned Security Framework, the proposal utilizes multi-domain and multi-tenant orchestration to automate and scale the deployment of FL-agents and AI-based anomaly detectors, enhancing reaction capabilities against cyber-attacks. The proposed FL architecture can be dynamically deployed across the B5G Continuum, utilizing a hierarchy of Network Intelligence orchestrators for real-time anomaly and security threat handling. Implementation includes FL enforcement operations for interoperability and extensibility, enabling dynamic deployment, configuration, and reconfiguration on demand. Performance validation of the proposed solution was conducted through dynamic orchestration, FL, and real-time anomaly detection processes using a practical test environment. Analysis of key performance metrics, leveraging the 5G-NIDD dataset, demonstrates the system’s capability for automatic and near real-time handling of anomalies and attacks, including real-time network monitoring and countermeasure implementation for mitigation.

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Keywords

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