Federated Network Intelligence Orchestration for Scalable and Automated FL-Based Anomaly Detection in B5G Networks
Pablo Fernández Saura1,*, 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: pablofs@um.es
(This article belongs to the Special Issue: Innovative Security for the Next Generation Mobile Communication and Internet Systems)
Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.051307
Received 01 March 2024; Accepted 20 May 2024; Published online 09 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.
Keywords
Federated learning; 6G; orchestration; anomaly detection; security policy
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