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

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 experime...

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Bibliographic Details
Published in:Automatic control and computer sciences Vol. 57; no. 8; pp. 1040 - 1046
Main Authors: Krundyshev, V. M., Markov, G. A., Kalinin, M. O., Semyanov, P. V., Busygin, A. G.
Format: Journal Article
Language:English
Published: Moscow Pleiades Publishing 01-12-2023
Springer Nature B.V
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Summary: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.
ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411623080114