学术报告通知
时间：2016年10月24日 下午15:30
地点：北航新主楼D639会议室
报告题目：Defending
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.
报告内容摘要：
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.
