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Machine Learning Inspired Fault Detection of Dynamical Networks

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AI 2022: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13728))

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Abstract

Dynamical networks are a framework commonly used to model large networks of interacting time-varying components such as power grids and epidemic disease networks. The connectivity structure of dynamical networks play a key role in enabling many interesting behaviours such as synchronisation and chimeras. However, dynamical networks can also be vulnerable to network attack, where the connectivity structure is externally altered. This can cause sudden failure and loss of stability in the network. The ability to detect these network attacks is useful in troubleshooting and preventing system failure. Recently, a backpropagation regression method inspired by RNN training algorithms was proposed to infer both local node dynamics and connectivity structure from measured node signals. This paper explores the application of backpropagation regression for fault detection in dynamical networks. We construct separate models for local dynamics and coupling structure to perform short-term freerun predictions. Due to the separation of models, abnormal increases in prediction error can be attributed to changes in the network structure. Automatic detection is achieved by comparing prediction error statistics across two windows that span a period before and after a network attack. This method is tested on a simulated dynamical network of chaotic Lorenz oscillators undergoing gradual edge corruption via three different processes: edge swapping, moving and deletion. We demonstrate that the correlation between increased prediction error and the occurrence of edge corruption can be used to reliably detect both the onset and approximate location of the attack within the network.

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Acknowledgements

E.T. is supported by a Robert and Maude Gledden Postgraduate Research Scholarship and Australian Government Research Training Program Scholarship at The University of Western Australia. M.S. and D.C.C. acknowledge the support of the Australian Research Council through the Centre for Transforming Maintenance through Data Science (grant number IC180100030), funded by the Australian Government.

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Authors and Affiliations

  1. Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Crawley, WA, 6009, Australia

    Eugene Tan, Thomas Stemler & Michael Small

  2. Department of Computer Science and Software Engineering, The University of Western Australia, Crawley, WA, 6009, Australia

    Débora C. Corrêa

  3. ARC Industrial Transformation Training Centre (Transforming Maintenance through Data Science), The University of Western Australia, Crawley, WA, 6009, Australia

    Débora C. Corrêa & Michael Small

Authors
  1. Eugene Tan
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  2. Débora C. Corrêa
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  3. Thomas Stemler
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Correspondence to Eugene Tan .

Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Haris Aziz

  2. University of Western Australia, Perth, WA, Australia

    Débora Corrêa

  3. University of Western Australia, Perth, WA, Australia

    Tim French

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Tan, E., Corrêa, D.C., Stemler, T., Small, M. (2022). Machine Learning Inspired Fault Detection of Dynamical Networks. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_33

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  • DOI: https://doi.org/10.1007/978-3-031-22695-3_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22694-6

  • Online ISBN: 978-3-031-22695-3

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