Development of the Article on Autonomous Federated Learning for Distributed Intrusion Detection Systems in Public Networks

As part of the innovative IDUNN Project, we are pleased to announce the development of an article titled “Autonomous Federated Learning for Distributed Intrusion Detection Systems in Public Networks.” This work focuses on the need for advanced Intrusion Detection Systems (IDS) to maintain security in highly interconnected networks, a growing necessity due to the rapid integration of IoT, cloud, and edge computing.

Successful AI-based IDS relies on high-quality data for model training. Despite the availability of numerous datasets from controlled settings, many are outdated and lack the representative data of network traffic dynamics typically seen in public networks. This paper aims to advance the understanding of designing testbed architectures for defense mechanisms within public networks.

At its core, this research introduces a unique testbed utilizing the connectivity of the panOULU Municipal public network in the city of Oulu, Finland. This experimental setup examines AI-driven security across the public network, leveraging edge-to-cloud infrastructures and incorporating Software-Defined Networking (SDN) and Network Function Virtualization (NFV) via the VMware vSphere platform.

During the training phase, a script classifies incoming packets as either benign or malicious based on well-defined local parameters and simulated attack scenarios. This labeled data is then used for training machine learning models within the Federated Learning framework, FED-ML. Subsequently, these models are evaluated on previously unseen data.

The entire procedure, from traffic gathering to model training, operates without human involvement. The evaluation dataset and testbed configuration we have made publicly available through this research can deepen our understanding of the challenges in safeguarding public networks, especially those that blend various technologies in diverse environments.

This article has been developed by University of Oulu