Federated Learning-Based Approach for Intrusion Detection in Computer Networks

• Alexsander Damaceno UFG
• Maria do Rosário C. Ribeiro UFG / INESC-TEC
• Antonio Oliveira-Jr UFG / Fraunhofer Portugal AICOS
• Renan R. de Oliveira UFG / IFG

DOI: https://doi.org/10.5753/erigo.2023.237320

Abstract

The rapid expansion of digital networks and the growing number of computer security incidents, the need for methods for preventing and detecting malicious activities becomes evident. Traditional approaches to network intrusion detection often face limitations in scalability, privacy, and adaptability. This article explores Federated Learning (FL) as a solution to address these challenges. By decentralizing the training process and preserving data privacy at the source, FL empowers network administrators to collaboratively build robust anomaly detection models without sharing sensitive information.

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