Detection of biasing attacks on distributed estimation networks
Detection of biasing attacks on distributed estimation networks
The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an $H_\infty$ approach.
The paper is to appear in Proceedings of the 55th IEEE Conference on Decision and Control, Las Vegas, December 2016
- UNSW Sydney Australia
- University of Melbourne Australia
- Australian Defence Force Academy Australia
- University of Illinois at Urbana Champaign United States
330, anzsrc-for: 46 Information and Computing Sciences, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, 004, anzsrc-for: 40 Engineering, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, FOS: Electrical engineering, electronic engineering, information engineering, anzsrc-for: 4606 Distributed Computing and Systems Software, 40 Engineering
330, anzsrc-for: 46 Information and Computing Sciences, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control, 004, anzsrc-for: 40 Engineering, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, FOS: Electrical engineering, electronic engineering, information engineering, anzsrc-for: 4606 Distributed Computing and Systems Software, 40 Engineering
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