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Automatic Control and Computer Sciences

Erschienen in:

01.12.2018

Detection of Threats in Cyberphysical Systems Based on Deep Learning Methods Using Multidimensional Time Series

verfasst von: M. O. Kalinin, D. S. Lavrova, A. V. Yarmak

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 8/2018

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Abstract—

A method for detecting anomalies in the work of cyberphysical systems by analyzing a multidimensional time series is proposed. The method is based on the use of neural network technologies to predict the values of the time series of the system data and to identify deviations between the predicted value and the current data obtained from the sensors and actuators. The results of experimental studies are presented, which testify to the effectiveness of the proposed solution.

Vorheriger Artikel Hierarchical Software-Defined Security Management for Large-Scale Dynamic Networks

Nächster Artikel A Use Case Analysis of Heterogeneous Semistructured Objects in Information Security Problems

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Titel: Detection of Threats in Cyberphysical Systems Based on Deep Learning Methods Using Multidimensional Time Series
verfasst von: M. O. Kalinin
D. S. Lavrova
A. V. Yarmak
Publikationsdatum: 01.12.2018
Verlag: Pleiades Publishing
Erschienen in: Automatic Control and Computer Sciences / Ausgabe 8/2018
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411618080151

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