Ensemble learning-enabled security anomaly identification for iot cyber-physical power systems

HIGHLIGHTS

• who: Hongjun Zhao et al. from the State Grid Xinjiang Electric Power Corporation Limited, Urumqi, China College of Automation and College of Artificial Intelligence, Nanjing University of Posts and have published the research: Ensemble Learning-Enabled Security Anomaly Identification for IoT Cyber-Physical Power Systems, in the Journal: Electronics 2022, 4043 of /2022/
• what: At present, the research on the identification methods of network attacks on the grid side mainly focuses on the identification of malicious data injection, and the coverage of attack types is insufficient. This research has the problem of whether the abnormal . . .
  

   

  

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