Cyber Threat Intelligence Sharing Scheme Based on Federated Learning for Network Intrusion Detection

The uses of machine learning (ML) technologies in the detection of network attacks have been proven to be effective when designed and evaluated using data samples originating from the same organisational network. However, it has been very challenging to design an ML-based detection system using heterogeneous network data samples originating from different sources and organisations. This is mainly due to privacy concerns and the lack of a universal format of datasets. In this paper, we propose a collaborative cyber threat intelligence sharing scheme to allow multiple organisations to join forces in the design, training, and evaluation of a robust ML-based network intrusion detection system. The threat intelligence sharing 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 and 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 the intelligence of other organisations while maintaining the privacy of its data internally. In this paper, the framework has been designed and evaluated using two key datasets in a NetFlow format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. In addition, two other common scenarios are considered in the evaluation process; a centralised training method where local data samples are directly 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 various benign and intrusive traffic types originating from multiple organisations without the need for inter-organisational data exchange.