Chapter 8 Intrusion Detection for IoT-based Context and Networks

Rosella Omana Mancilla; Francesca Costantino; Cesar Caramazana Zarzosa; Juan Manuel Vera Diaz

Chapter 8 Intrusion Detection for IoT-based Context and Networks

By Rosella Omana Mancilla, ENGINEERING Ingegneria Informatica S.p.A., Piazzale dell’Agricoltura, 24 – 00144 Rome, Italy, rosellaomana.mancilla@eng.it | Francesca Costantino, ENGINEERING Ingegneria Informatica S.p.A., Piazzale dell’Agricoltura, 24 – 00144 Rome, Italy, francesca.costantino@eng.it | Cesar Caramazana Zarzosa, BDS R&D Spain, Atos IT Solutions and Services, Iberia, C/ Albarracin 25, Madrid, Spain, cesar.cararnazana@atos.net | Juan Manuel Vera Diaz,

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Published: 07 May 2025

Abstract

Exploring the implementation of a robust Monitoring and Intrusion Detection System (IDS) within the ERATOSTHENES project. It details the integration of machine learning techniques for enhanced threat detection in complex IoT environments. The chapter also discusses the development and implementation of FedLPy, a federated learning approach for collaborative threat detection on edge devices, enhancing network security through distributed intelligence.