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Binary Classification of Fractal Time Series by Machine Learning Methods

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Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1020))

Abstract

The paper considers the binary classification of time series based on their fractal properties by machine learning. This approach is applied to the realizations of normal and attacked network traffic, which allows to detect DDoS-attacks. A comparative analysis of the results of the classification by the random forest and neural network - fully connected multi-layer perceptron is carried out. The statistical, fractal and recurrence characteristics calculated from each time series were used as features for classification. The analysis showed that both methods provide highly accurate of classification and can be used to detect attacks in intrusion detection systems.

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Authors and Affiliations

  1. Kharkiv National University of Radioelectronics, Kharkiv, Ukraine

    Lyudmyla Kirichenko, Tamara Radivilova & Vitalii Bulakh

Authors
  1. Lyudmyla Kirichenko
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  2. Tamara Radivilova
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  3. Vitalii Bulakh
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Corresponding author

Correspondence to Lyudmyla Kirichenko .

Editor information

Editors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

  2. Jan Evangelista Purkyně University in Ústi and Labem, Usti and Labem, Czech Republic

    Sergii Babichev

  3. Lublin University of Technology, Lublin, Poland

    Waldemar Wójcik

  4. IT Step University, Lviv, Ukraine

    Olena Vynokurova

  5. Kherson National Technical University, Kherson, Ukraine

    Svetlana Vyshemyrskaya

  6. Kherson National Technical University, Kherson, Ukraine

    Svetlana Radetskaya

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Kirichenko, L., Radivilova, T., Bulakh, V. (2020). Binary Classification of Fractal Time Series by Machine Learning Methods. In: Lytvynenko, V., Babichev, S., Wójcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2019. Advances in Intelligent Systems and Computing, vol 1020. Springer, Cham. https://doi.org/10.1007/978-3-030-26474-1_49

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