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时间：2016年10月28日9:30

地点：21#426

主题：Defending Against False Data Injection Attacks on Power System State Estimation

报告人：邓瑞龙

主办单位：科学技术研究院

承办单位：计算机科学与技术学院

报告人简介：

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.