Internet of Things intrusion Detection: Centralized, On-Device, or Federated Learning?

With the ever increasing number of cyber-attacks, internet of Things (ioT) devices are being exposed to serious malware, attacks, and malicious activities alongside their development. While past research has been focused on centralized intrusion detection assuming the existence of a central entity to store and perform analysis on data from all participant devices, these approaches cannot scale well with the fast growth of ioT connected devices and introduce a single-point failure risk that may compromise data privacy. Moreover, with data being widely spread across large networks of connected devices, decentralized computations are very much in need. in this context, we propose in this article a Federated Learning based scheme for ioT intrusion detection that maintains data privacy by performing local training and inference of detection models. in this scheme, not only privacy can be assured, but also devices can benefit from their peers' knowledge by communicating only their updates with a remote server that aggregates the latter and shares an improved detection model with participating devices. We perform thorough experiments on an NSL-KDD dataset to evaluate the efficiency of the proposed approach. Experimental results and empirical analysis explore the robustness and advantages of the proposed Federated Learning detection model by reaching an accuracy close to that of the centralized approach and outperforming the distributed unaggregated on-device trained models.