Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230700077-8.doi: 10.11896/jsjkx.230700077

• Information Security • Previous Articles     Next Articles

Study on Smart Grid AMI Intrusion Detection Method Based on Federated Learning

LIU Dongqi1, ZHANG Qiong1, LIANG Haolan1,2, ZHANG Zidong1, ZENG Xiangjun1   

  1. 1 College of Electrical and Information Engineering,Changsha University of Science & Technology,Changsha 410114,China
    2 College of Electrical and Information Engineering,Hunan Institute of Engineering,Xiangtan,Hunan 411104,China
  • Published:2024-06-06
  • About author:LIU Dongqi,born in 1986,Ph.D,asso-ciate professor.His main research in-terests include intelligent grid information processing and distributed collaborative control.
    LIANG Haolan,born in 1993,Ph.D candidate.Her main research interest is power system information security.
  • Supported by:
    National Natural Science Foundation of China(52177068),National Key R&D Program of China(2018YFB0904900),Research Project of Hunan Provincial Department of Education(21C0577) and Scientific Research and Innovation Project of Changsha University of Science and Technology(CXCLY2022076).

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Abstract: Advanced metering infrastructure(AMI) is a key link in building smart grid and ubiquitous electric IoT.With the application 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 integrates 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 dataset,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,reducing the risk of data privacy disclosure.

Key words: AMI, Federated learning, Intrusion detection, Cloud edge collaboration, Decision tree

CLC Number: 

  • TP399
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