Study on Smart Grid AMI Intrusion Detection Method Based on Federated Learning
Advanced metering infrastructure(AMI)is a key link in building smart grid and ubiquitous electric IoT.With the ap-plication of mass terminal access and heterogeneous communication network components,the risk of network attacks on AMI is greatly increased.For the problems of traditional AMI network attack intrusion detection methods,such as excessive computing pressure of the main station,weak disaster resistance ability and insufficient recognition accuracy,an AMI intrusion detection method based on federated learning is proposed.Firstly,the federated learning intrusion detection model for AMI is constructed,and the federated learning framework is integrated into the model.Then,a lightweight intrusion detection algorithm that in-tegrates decision tree on the edge side is designed,and a cross-platform cloud-edge collaborative joint training method is proposed to realize cross-platform experience sharing and improve intrusion detection performance.Finally,based on the NSL-KDD data-set,simulation results show that compared with the centralized and federated learning fusion neural network intrusion detection models,the accuracy of the proposed method can reach 99.76％,and the false positive rate is only 0.17％.At the same time,the detection time is reduced,the communication efficiency is improved.It also ensures that data does not leave the local area,reduc-ing the risk of data privacy disclosure.
AMIFederated learningIntrusion detectionCloud edge collaborationDecision tree
NETL
NSTL
万方数据
