  Cyberattacks are increasingly threatening networked systems, often with the
emergence of new types of unknown (zero-day) attacks and the rise of vulnerable
devices. Such attacks can also target multiple components of a Supply Chain,
which can be protected via Machine Learning (ML)-based Intrusion Detection
Systems (IDSs). However, the need to learn large amounts of labelled data often
limits the applicability of ML-based IDSs to cybersystems that only have access
to private local data, while distributed systems such as Supply Chains have
multiple components, each of which must preserve its private data while being
targeted by the same attack To address this issue, this paper proposes a novel
Decentralized and Online Federated Learning Intrusion Detection (DOF-ID)
architecture based on the G-Network model with collaborative learning, that
allows each IDS used by a specific component to learn from the experience
gained in other components, in addition to its own local data, without
violating the data privacy of other components. The performance evaluation
results using public Kitsune and Bot-IoT datasets show that DOF-ID
significantly improves the intrusion detection performance in all of the
collaborating components, with acceptable computation time for online learning.
