  Network Intrusion Detection Systems (IDS) aim to detect the presence of an
intruder by analyzing network packets arriving at an internet connected device.
Data-driven deep learning systems, popular due to their superior performance
compared to traditional IDS, depend on availability of high quality training
data for diverse intrusion classes. A way to overcome this limitation is
through transferable learning, where training for one intrusion class can lead
to detection of unseen intrusion classes after deployment. In this paper, we
provide a detailed study on the transferability of intrusion detection. We
investigate practical federated learning configurations to enhance the
transferability of intrusion detection. We propose two techniques to
significantly improve the transferability of a federated intrusion detection
system. The code for this work can be found at
https://github.com/ghosh64/transferability.
