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The vast increase of Internet of Things (IoT) technologies and theever-evolving attack vectors have increased cyber-security risks dramatically.A common approach to implementing AI-based Intrusion Detection systems (IDSs)in distributed IoT systems is in a centralised manner. However, this approachmay violate data privacy and prohibit IDS scalability. Therefore, intrusiondetection solutions in IoT ecosystems need to move towards a decentraliseddirection. Federated Learning (FL) has attracted significant interest in recentyears due to its ability to perform collaborative learning while preservingdata confidentiality and locality. Nevertheless, most FL-based IDS for IoTsystems are designed under unrealistic data distribution conditions. To thatend, we design an experiment representative of the real world and evaluate theperformance of an FL-based IDS. For our experiments, we rely on TON-IoT, arealistic IoT network traffic dataset, associating each IP address with asingle FL client. Additionally, we explore pre-training and investigate variousaggregation methods to mitigate the impact of data heterogeneity. Lastly, webenchmark our approach against a centralised solution. The comparison showsthat the heterogeneous nature of the data has a considerable negative impact onthe model's performance when trained in a distributed manner. However, in thecase of a pre-trained initial global FL model, we demonstrate a performanceimprovement of over 20% (F1-score) compared to a randomly initiated globalmodel.

Federated Deep Learning for Intrusion Detection in IoT Networks