KFRNN: An Effective False Data Injection Attack Detection in Smart Grid Based on Kalman Filter and Recurrent Neural Network

Yufeng Wang, Zhihao Zhang, Jianhua Ma, Qun Jin

Research output: Contribution to journalArticlepeer-review


The smart grid is now increasingly dependent on smart devices to operate, which leaves space for cyber attacks. Especially, the intentionally designed false data injection attack (FDIA) can successfully bypass the traditional measurement residual-based bad data detection scheme. Considering that the smart grid data naturally contain linear and nonlinear components, inspired by parallel ensemble learning, especially by stacking method, this paper presents an effective two-level learner based FDIA detection scheme using Kalman filter and recurrent neural network (KFRNN). The first-level includes two base learners, in which Kalman filter is used for state prediction to fit linear data, and RNN is used to fit nonlinear data feature. The second-level learner uses the fully connected layer and back propagation (BP) module to adaptively combine the results of two base learners. Then, through fitting Weibull distribution of the sum of square errors (SSEs) between the observed measurements and the predicted measurements, the dynamic threshold is obtained to judge whether FDIA occurs or not. Comprehensive simulation results show that our scheme has better performance than other neural network based and ensemble learning based FDIA detection schemes.

Original languageEnglish
JournalIEEE Internet of Things Journal
Publication statusAccepted/In press - 2021


  • Data models
  • Ensemble learning.
  • False data injection attack
  • Kalman filter
  • Kalman filters
  • Predictive models
  • Reactive power
  • Recurrent neural network
  • Recurrent neural networks
  • Smart grid
  • Smart grids
  • Transmission line measurements

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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