hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Detection of Application-Layer DDoS Attacks Produced by Various Freely Accessible Toolkits Using Machine Learning
Soran University, Soran, Iraq.
Soran University, Soran, Iraq; Iran University of Science and Technology, Tehran, Iran.
Halmstad University, School of Information Technology.ORCID iD: 0000-0002-2874-6256
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 51810-51819Article in journal (Refereed) Published
Abstract [en]

Distributed Denial of Service (DDoS) attacks are a growing threat to online services, and various methods have been developed to detect them. However, past research has mainly focused on identifying attack patterns and types, without specifically addressing the role of freely available DDoS attack tools in the escalation of these attacks. This study aims to fill this gap by investigating the impact of the easy availability of DDoS attack tools on the frequency and severity of attacks. In this paper, a machine learning solution to detect DDoS attacks is proposed, which employs a feature selection technique to enhance its speed and efficiency, resulting in a substantial reduction in the feature subset. The provided evaluation metrics demonstrate that the model has a high accuracy level of 99.9%, a precision score of 96%, a recall score of 98%, and an F1 score of 97%. Moreover, the examination found that by utilizing a deliberate approach for feature selection, our model's efficacy was massively raised. © 2013 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2023. Vol. 11, p. 51810-51819
Keywords [en]
DDoS, DDoS tools, deep learning, machine learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-51393DOI: 10.1109/ACCESS.2023.3280122ISI: 001006296300001Scopus ID: 2-s2.0-85161050269OAI: oai:DiVA.org:hh-51393DiVA, id: diva2:1788059
Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2023-08-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Fazeli, Mahdi

Search in DiVA

By author/editor
Fazeli, Mahdi
By organisation
School of Information Technology
In the same journal
IEEE Access
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 83 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf