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Title: Machine Learning in Classification Time Series with Fractal Properties
Authors: Кіріченко, Л. О.
Булах, В. А.
Радівілова, Т. А.
Keywords: fractal time series
binomial stochastic cascade
classification of time series
Hurst exponent
random forest
detecting distributed denial-of-service attacks
Issue Date: 2019
Citation: Kirichenko L. Machine Learning in Classification Time Series with Fractal Properties / L. Kirichenko, V. Bulakh, T. Radivilova // Data. – 2019. – vol.4(1) 5. – P. 1–13.
Abstract: The article presents a novel method of fractal time series classification by meta-algorithms based on decision trees. The classification objects are fractal time series. For modeling, binomial stochastic cascade processes are chosen. Each class that was singled out unites model time series with the same fractal properties. Numerical experiments demonstrate that the best results are obtained by the random forest method with regression trees. A comparative analysis of the classification approaches, based on the random forest method, and traditional estimation of self-similarity degree are performed. The results show the advantage of machine learning methods over traditional time series evaluation. The results were used for detecting denial-of-service (DDoS) attacks and demonstrated a high probability of detection.
Appears in Collections:Кафедра прикладної математики (ПМ)

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