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Cyberattack Detection in the Industrial Internet of Things Based on the Computation Model of Hierarchical Temporal Memory

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Abstract

This study considers the problem of detecting network anomalies caused by computer attacks in the networks of the industrial Internet of things. To detect anomalies, a new method is proposed, built using a hierarchical temporal memory (HTM) computation model based on the neocortex model. An experimental study of the developed method of detecting computer attacks based on the HTM model showed the superiority of the developed solution over the LSTM analog. The developed prototype of the anomaly detection system provides continuous training on unlabeled data sets in real time, takes into account the current network context, and applies the accumulated experience by supporting the memory mechanism.

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Funding

The research is funded by the Ministry of Science and Higher Education of the Russian Federation as part of the World-Class Research Center program: Advanced Digital Technologies (contract no. 075-15-2022-311 dated April 20, 2022).

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Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    V. M. Krundyshev, M. O. Kalinin, P. V. Semyanov & A. G. Busygin

  2. Jet Infosystems, 127015, Moscow, Russia

    G. A. Markov

Authors
  1. V. M. Krundyshev
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  2. G. A. Markov
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  3. M. O. Kalinin
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  4. P. V. Semyanov
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  5. A. G. Busygin
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Corresponding author

Correspondence to V. M. Krundyshev.

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Krundyshev, V.M., Markov, G.A., Kalinin, M.O. et al. Cyberattack Detection in the Industrial Internet of Things Based on the Computation Model of Hierarchical Temporal Memory. Aut. Control Comp. Sci. 57, 1040–1046 (2023). https://doi.org/10.3103/S0146411623080114

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