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Detection of the botnets’ low-rate DDoS attacks based on self-similarity

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 4 (2020)

DOI
10.11591/ijece.v10i4.pp3651-3659
ISSN
2722-2578, 2088-8708
Published
2020-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.

Collaboration network of the authors

5 authors · 10 co-authorship links

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  • Khmelnytskyi National University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Detection of the botnets’ low-rate DDoS attacks based on self-similarity</dc:title><dc:creator>Lysenko, Sergii</dc:creator><dc:creator>Bobrovnikova, Kira</dc:creator><dc:creator>Matiukh, Serhii</dc:creator><dc:creator>Hurman, Ivan</dc:creator><dc:creator>Savenko, Oleg</dc:creator><dc:description>&lt;jats:p&gt;An article presents the approach for the botnets’ low-rate a DDoS-attacks detection based on the botnet’s behavior in the network. Detection process involves the analysis of the network traffic, generated by the botnets’ low-rate DDoS attack. Proposed technique is the part of botnets detection system – BotGRABBER system. The novelty of the paper is that the low-rate DDoS-attacks detection involves not only the network features, inherent to the botnets, but also network traffic self-similarity analysis, which is defined with the use of Hurst coefficient. Detection process consists of the knowledge formation based on the features that may indicate low-rate DDoS attack performed by a botnet; network monitoring, which analyzes information obtained from the network and making conclusion about possible DDoS attack in the network; and the appliance of the security scenario for the corporate area network’s infrastructure in the situation of low-rate attacks.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v10i4.pp3651-3659</dc:identifier><dc:identifier>10.11591/ijece.v10i4.pp3651-3659</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 4 (2020); 3651</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/20780/14018</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>