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Volume 2018 (2018), Article ID 1240149, 8 pages
Research Article

Attack Detection/Isolation via a Secure Multisensor Fusion Framework for Cyberphysical Systems

1Concordia Institute for Information System Engineering, Concordia University, Montreal, QC, H3H-1M8, Canada
2College of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Correspondence should be addressed to Arash Mohammadi

Received 14 September 2017; Accepted 9 January 2018; Published 11 February 2018

Academic Editor: Carlos Gershenson

Copyright © 2018 Arash Mohammadi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Motivated by rapid growth of cyberphysical systems (CPSs) and the necessity to provide secure state estimates against potential data injection attacks in their application domains, the paper proposes a secure and innovative attack detection and isolation fusion framework. The proposed multisensor fusion framework provides secure state estimates by using ideas from interactive multiple models (IMM) combined with a novel fuzzy-based attack detection/isolation mechanism. The IMM filter is used to adjust the system’s uncertainty adaptively via model probabilities by using a hybrid state model consisting of two behaviour modes, one corresponding to the ideal scenario and one associated with the attack behaviour mode. The state chi-square test is then incorporated through the proposed fuzzy-based fusion framework to detect and isolate potential data injection attacks. In other words, the validation probability of each sensor is calculated based on the value of the chi-square test. Finally, by incorporation of the validation probability of each sensor, the weights of its associated subsystem are computed. To be concrete, an integrated navigation system is simulated with three types of attacks ranging from a constant bias attack to a non-Gaussian stochastic attack to evaluate the proposed attack detection and isolation fusion framework.