2016/6/24, Quickest Change Detection with Observation Scheduling, Optimal Linear Cyber-Attack on Remote State Estimation
发布人: 发布时间: 2016-06-23 访问次数: 9

Date:2016/6/24
Time:9:30-11:30
Place:Room 207, Experiment fifteen buildings

Speaker: Xiaoqiang Ren
Title: Quickest Change Detection with Observation Scheduling
Abstract: The quickest change detection problem is to detect an abrupt change event as quickly as possible subject to constraints on false detection. Unlike the classical problem, where the decision maker can access only one sequence of observations, in this paper, the decision maker chooses one of two different sequences of observations at each time instant. The information quality and sampling cost of the two sequences of observations are different. We present an asymptotically optimal joint design of observation scheduling policy and stopping time such that the detection delay is minimized subject to constraints on both average run length to false alarm (ARLFA) and average cost per sample. The observation scheduling policy has a threshold structure and the detection scheme is a variant of the cumulative sum test where the detection statistic stochastically crosses the threshold that is used to switch observation modes. We further study the decentralized case in a multi-channel setting. We show that if each sensor uses the proposed observation scheduling policy locally and the fusion center uses the N_Malgorithm, by which the center declares the change when the sum of the sensors' local detection statistics crosses a certain threshold, the detection delay is asymptotically minimized for any possible combination of the affected sensors subject to constraints on both global ARLFA and average cost per sample at each sensor node.

Speaker: Ling Shi
Title: Optimal Linear Cyber-Attack on Remote State Estimation
Abstract: We consider malicious cyber attacks in a remote state estimation application where a smart sensor node transmits data to a remote estimator equipped with a false data detector. It is assumed that all the sensor data can be observed and modified by the malicious attacker and a residue-based detection algorithm is used at the remote side to detect data anomalies. We propose a linear deception attack strategy and present the corresponding feasibility constraint which guarantees that the attacker is able to successfully inject false data without being detected. The evolution of the estimation error covariance at the remote estimator is derived and the degradation of system performance under the proposed linear attack policy is analyzed. Furthermore, we obtain a closed-form expression of the optimal attack strategy among all linear attacks. Comparison of attack strategies through simulated examples are provided.


Individual Introduction:

Xiaoqiang Ren was graduated from the Department of Automation of Zhejiang University. Now he is Ph.D student of Hong Kong University of Science and Technology. His research interests include sequential detection, network control and estimation, as well as information systems security and so on.

Ling Shi received the B.S. degree in electrical and electronic engineering from Hong Kong University of Science and Technology, Kowloon, Hong Kong, in 2002 and the Ph.D. degree in Control and Dynamical Systems from California Institute of Technology, Pasadena, CA, USA, in 2008. He is currently an associate professor at the Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology. His research interests include cyber-physical systems security, networked control systems, sensor scheduling, and event-based state estimation. He has been serving as a subject editor for International Journal of Robust and Nonlinear Control from 2015. He also served as an associate editor for a special issue on Secure Control of Cyber Physical Systems in the IEEE Transactions on Control of Network Systems in 2015-2016.