A Statistical Learning-Based Algorithm for Topology Verification in Natural Gas Networks Based on Noisy Sensor Measurements

@article{Wang2020ASL,
  title={A Statistical Learning-Based Algorithm for Topology Verification in Natural Gas Networks Based on Noisy Sensor Measurements},
  author={Zisheng Wang and Rick S. Blum},
  journal={IEEE Transactions on Information Forensics and Security},
  year={2020},
  volume={15},
  pages={3653-3666}
}
Accurate knowledge of natural gas network topology is critical for the proper operation of natural gas networks. Failures, physical attacks, and cyber attacks can cause the actual natural gas network topology to differ from what the operator believes to be present. Incorrect topology information misleads the operator to apply inappropriate control causing damage and lack of gas supply. Several methods for verifying the topology have been suggested in the literature for electrical power… 
Elimination of Undetectable Attacks on Natural Gas Networks
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This paper proposes a countermeasure to eliminate undetectable attacks on natural gas networks in a signal processing perspective by describing the steady-state mathematical model and sensor measurements and presents an example that describes how an operator can be misled if the proposed countermeasures are not applied.

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