Abstract:

With the advent of the era of big data, data has become an important strategic resource for social development. However, with the increasing complexity of the network environment, privacy leakage and malicious attacks emerge in an endless stream. As a new data sharing model, federated learning can share data on the premise of protecting data privacy. In particular, it can effectively solve the shortcomings of traditional intrusion detection model. Therefore, this paper proposed an intrusion detection mechanism based on federated learning. This paper first introduced the structure and characteristics of federated learning and intrusion detection model, And deeply analyzed the feasibility of intrusion detection mechanism based on federated learning to effectively improve the detection accuracy and efficiency. The prototype system was developed through the requirement analysis and design of the model, and the simulation experimented with function programming. It is found that the federated learning mechanism can realize the sharing of multi-party attack logs on the premise of ensuring the data privacy security of participating clients. At the same time, through the control experiments of multiple groups of control variables, it is proved that the intrusion detection mechanism based on federated learning has significantly improved the detection accuracy and efficiency.

Key words: federated learning, malicious attacks, intrusion detection, network security