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Automatic Control and Computer Sciences

Erschienen in:

01.12.2023

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

verfasst von: V. M. Krundyshev, G. A. Markov, M. O. Kalinin, P. V. Semyanov, A. G. Busygin

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 8/2023

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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.

Vorheriger Artikel Stability Analysis of the Architecture of Messaging Systems with a Decentralized Node Structure

Nächster Artikel Early Detection of Network Attacks Based on Weight-Insensitive Neural Networks

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Titel: Cyberattack Detection in the Industrial Internet of Things Based on the Computation Model of Hierarchical Temporal Memory
verfasst von: V. M. Krundyshev
G. A. Markov
M. O. Kalinin
P. V. Semyanov
A. G. Busygin
Publikationsdatum: 01.12.2023
Verlag: Pleiades Publishing
Erschienen in: Automatic Control and Computer Sciences / Ausgabe 8/2023
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411623080114

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