A Federated Approach to Enhance Calibration of Distributed ML-Based Intrusion Detection Systems

Jacopo Talpini, Nicoló Civiero, Fabio Sartori, Marco Savi

Abstract: Network intrusion detection systems (IDSs) are a major component for network security, aimed at protecting network-accessible endpoints, such as IoT devices, from malicious activities that compromise confidentiality, integrity, or availability within the network infrastructure. Machine Learning models are becoming a popular choice for developing an IDS, as they can handle large volumes of network traffic and identify increasingly sophisticated patterns. However, traditional ML methods often require a centralized large dataset thus raising privacy and scalability concerns. Federated Learning (FL) offers a promising solution by enabling a collaborative training of an IDS, without sharing raw data among clients. However, existing research on FL-based IDSs primarily focuses on improving accuracy and detection rates, while little or no attention is given to a proper estimation of the model’s uncertainty in making predictions. This is however fundamental to increase the model’s reliability

External IDs:dblp:conf/icaart/TalpiniCSS25