{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T06:11:17Z","timestamp":1762409477062,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T00:00:00Z","timestamp":1545955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/data4010005","type":"journal-article","created":{"date-parts":[[2018,12,28]],"date-time":"2018-12-28T11:52:42Z","timestamp":1545997962000},"page":"5","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Machine Learning in Classification Time Series with Fractal Properties"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2780-7993","authenticated-orcid":false,"given":"Lyudmyla","family":"Kirichenko","sequence":"first","affiliation":[{"name":"Department of Applied mathematics, Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5975-0269","authenticated-orcid":false,"given":"Tamara","family":"Radivilova","sequence":"additional","affiliation":[{"name":"Department of Infocommunication Engineering, Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine"}]},{"given":"Vitalii","family":"Bulakh","sequence":"additional","affiliation":[{"name":"Department of Applied mathematics, Kharkiv National University of Radio Electronics, Kharkiv 61166, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12:1","DOI":"10.1145\/2379776.2379788","article-title":"Time series data mining","volume":"45","author":"Esling","year":"2012","journal-title":"ACM Comput. 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