  The uses of Machine Learning (ML) in detection of network attacks have been
effective when designed and evaluated in a single organisation. However, it has
been very challenging to design an ML-based detection system by utilising
heterogeneous network data samples originating from several sources. This is
mainly due to privacy concerns and the lack of a universal format of datasets.
In this paper, we propose a collaborative federated learning scheme to address
these issues. The proposed framework allows multiple organisations to join
forces in the design, training, and evaluation of a robust ML-based network
intrusion detection system. The threat intelligence scheme utilises two
critical aspects for its application; the availability of network data traffic
in a common format to allow for the extraction of meaningful patterns across
data sources. Secondly, the adoption of a federated learning mechanism to avoid
the necessity of sharing sensitive users' information between organisations. As
a result, each organisation benefits from other organisations cyber threat
intelligence while maintaining the privacy of its data internally. The model is
trained locally and only the updated weights are shared with the remaining
participants in the federated averaging process. The framework has been
designed and evaluated in this paper by using two key datasets in a NetFlow
format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. Two other common scenarios
are considered in the evaluation process; a centralised training method where
the local data samples are shared with other organisations and a localised
training method where no threat intelligence is shared. The results demonstrate
the efficiency and effectiveness of the proposed framework by designing a
universal ML model effectively classifying benign and intrusive traffic
originating from multiple organisations without the need for local data
exchange.
