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

• 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
• Subjects: Anomalies; Cerebral cortex; Computation; Computer Science; Control Structures and Microprogramming; Industrial applications; Internet of Things; HTM; sparse distributed representations; neocortex; artificial intelligence; hierarchical temporal memory; cyberattacks; real-time training
• ISSN: 0146-4116; 1558-108X
• DOI: 10.3103/S0146411623080114

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.