Defending Against False Data Injection Attacks on Power System State Estimation学术报告通知



时间:2016年10月24日 下午15:30



  Against False Data Injection Attacks on Power System State Estimation

主讲人:Dr. Ruilong Deng

  (AITF Postdoctoral Fellow, University of

Alberta, Canada)

主讲人Ruilong Deng简介

  Ruilong Deng (S’11-M’14) received the B.Sc. and Ph.D. degrees both in Control Science

and Engineering from Zhejiang University, China, in 2009 and 2014,

respectively. He was a Visiting Scholar at Simula Research Laboratory, Norway,

in 2011, and the University of Waterloo, Canada, from 2012 to 2013. He was a

Research Fellow at Nanyang Technological University, Singapore, from 2014 to

2015. Currently, he is an AITF Postdoctoral Fellow with the Department of

Electrical and Computer Engineering, University of Alberta, Canada. His

research interests include smart grid, cyber security, and wireless sensor

network. Dr. Deng currently serves as an Editor for IEEE/KICS Journal of

Communications and Networks, and a Guest Editor for IEEE Transactions on

Emerging Topics in Computing and Journal of Computer Networks and

Communications (Hindawi). He also serves/served as a Technical Program

Committee (TPC) Member for IEEE GLOBECOM, IEEE ICC, IEEE SmartGridComm, EAI

SGSC, etc. He is the recipient of the IEEE PES-GM 2016 Best Conference Papers

Award, and the author of 3 ESI Highly Cited Papers.


  This talk investigates the problem of defending against false data injection (FDI)

attacks on power system state estimation. Although many research works have

been previously reported on addressing the same problem, yet most of them made

a very strong assumption that some meter measurements can be absolutely

protected. To address the problem practically, a reasonable approach is to

assume whether or not a meter measurement could be compromised by an adversary

does depend on the defense budget deployed by the defender on the meter. From

this perspective, our contributions focus on designing the least-budget defense

strategy to protect power systems against FDI attacks. In addition, we also

extend to investigate choosing which meters to be protected and determining how

much defense budget to be deployed on each of these meters. We further

formulate the meter selection problem as a mixed integer nonlinear programming

problem, which can be efficiently tackled by Benders’ Decomposition. Finally,

extensive simulations are conducted on IEEE test power systems to demonstrate

the advantages of the proposed approach in terms of computing time and solution

quality, especially for large-scale power systems.