Data Privacy Security Guaranteed Network Intrusion Detection System Based on Federated Learning

Abstract

With the development of computer software, the amount of network data has increased geometrically. Therefore, how to quickly identify attacks from a large amount of network information is a meaningful research direction. The intrusion detection system (IDS) is the core contributor to protecting the host from attack. It can distinguish the characteristics of intrusion behavior and the intrusion action from the data of the host. However, with the huge increase in the amount of data now, the efficiency of identifying data characteristics is getting lower and lower. In addition, smart terminal equipment such as notebooks, smart phones and wearable devices are also emerging, and these devices are connected to the internet through wireless or wired means. The physical data generated by terminal equipment involves huge amount of personal sensitive data, which poses a challenge to data privacy and security. Federated learning, as a new type of distributed learning framework, allows training data to be shared among multiple participants without revealing their data privacy. In order to solve the problem of privacy data in intrusion detection,, this paper proposes a network intrusion detection method based on federated learning and conducting experiments on the UNSW-NB15 dataset and CICIDS2018 dataset. The simulation results show that the method proposed in this paper can protect data privacy under the premise of achieving acceptable accuracy of intrusion traffic identification.