|
|
Time Series Analysis
Budai, Samuel ; Bartík, Vladimír (oponent) ; Burgetová, Ivana (vedoucí práce)
This thesis deals with the issue of time series analysis and its use in the detection of anomalies in industrial networks. AR-X, ARIMA, SARIMA, Random Forest, Facebook Prophet and XGB Boost algorithms were used in the solution to create prediction models. In addition, the work includes the implementation of an algorithm for detecting anomalies from prediction models as well as solving the problem of high seasonal period in the case of the SARIMA algorithm. Through the conducted research, it was found that with the use of selected algorithms, it is possible to predict industrial traffic for the purpose of detection, within which up to 90% of attacks were detected. The work also provides a solution to a high seasonal period using partial time series. These results allow the experimental integration of prediction-based detection into real industrial networks.
|
host ::
RSS.