Lecture: Relentless Attacks against Remote State Estimation in Smart Grids by Dr. Cheng Long

From:admin 2021-10-08

Relentless Attacks against Remote State Estimation in Smart Grids


Speaker: Cheng Long



16:00-18:00 Sept 30, 2021



7215 No. 7 Teaching Building



The smart grid is a nationwide networked system that applies information technology to enable two-way information flows to deliver electricity intelligently and reliably. However, while improving efficiency, the bidirectional communication also incurs invasions. The smart grid is proved to be more vulnerable to false data injection attacks than the traditional grids. This talk mainly focuses on the attack methods against the measurement collection and the remote state estimation in the smart grid. Theoretical analysis has been conducted to show the constructed false data injection attack can bypass the Kalman-based detection method. Furthermore, the countermeasures through the distributed control approach have been proposed to reduce the harm caused by the constructed false data injection attack.


Brief Introduction of the Speaker:

Cheng Long, Researcher, PhD. Supervisor, Institute of Automation, Chinese Academy of Sciences


Research Field:

Intelligent Control and Robot


Personal Experience:

2013-12--2014-03    Visiting Scholar, University of California, Riverside

2010-09--2011-03    Northeastern University (USA, Boston) Postdoctoral Research Associate

2010-03--2010-09    University of Saskatchewan (Canada, Saskatoon) Postdoctoral Research Associate

2004-09—2009-07   Doctor of Engineering, Institute of Automation, Chinese Academy of Sciences

2000-09—2004-07   Bachelor of Engineering, Nankai University




Winner of National Outstanding Youth Fund, 2014

Outstanding Young Scientists Program of Chinese Academy of Sciences, 2014

Beijing Science and Technology Award, First Prize, 2013

Beijing Science and Technology Award, Third Prize, 2014

Best Paper Award of IEEE International Conference on Information and Automation, 2013

IEEE Transactions on Neural Networks Outstanding Paper Award,2013