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Machine Recognition of DDoS Attacks Using Statistical Parameters

Author

Listed:
  • Juraj Smiesko

    (Department of InfoComm Networks, Faculty of Management Science and Informatics, University of Zilina, 010 26 Zilina, Slovakia)

  • Pavel Segec

    (Department of InfoComm Networks, Faculty of Management Science and Informatics, University of Zilina, 010 26 Zilina, Slovakia)

  • Martin Kontsek

    (Department of InfoComm Networks, Faculty of Management Science and Informatics, University of Zilina, 010 26 Zilina, Slovakia)

Abstract

As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic.

Suggested Citation

  • Juraj Smiesko & Pavel Segec & Martin Kontsek, 2023. "Machine Recognition of DDoS Attacks Using Statistical Parameters," Mathematics, MDPI, vol. 12(1), pages 1-30, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:142-:d:1311430
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    References listed on IDEAS

    as
    1. Zhengmin Xia & Songnian Lu & Junhua Tang, 2010. "Note on Studying Change Point of LRD Traffic Based on Li's Detection of DDoS Flood Attacking," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-14, June.
    2. Georgia Zournatzidou & Christos Floros, 2023. "Hurst Exponent Analysis: Evidence from Volatility Indices and the Volatility of Volatility Indices," JRFM, MDPI, vol. 16(5), pages 1-15, May.
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