View in EDS PDF Full Text

Botnet Detection: A System for Identifying DGA-based Botnets Using LightGBM.

Saved in:
Bibliographic Details
Title: Botnet Detection: A System for Identifying DGA-based Botnets Using LightGBM.
Authors: Mohamad, Mumtazimah1 mumtaz@unisza.edu.my, Hamid, Nazirah Abd1 nazirah@unisza.edu.my, Ghaleb, Sanaa1 sanaaabduljabbar@unisza.edu.my, Satar, Siti Dhalila Mohd1 sitidhalila@unisza.edu.my, Safei, Suhailan2 suhailan@unisza.edu.my, Wan Hamzah, Wan Mohd Amir Fazamin1 amirfazamin@unisza.edu.my
Source: IAENG International Journal of Applied Mathematics. Jun2026, Vol. 56 Issue 6, p2291-2300. 10p.
Subjects: Botnets, Machine learning, Internet security, Boosting algorithms, Anomaly detection (Computer security)
Abstract: Botnets using domain generation algorithms (DGAs) pose a major challenge to anomaly detection, as they generate large numbers of random domains to evade blacklists. Traditional methods struggle to classify these domains effectively due to their dynamic nature. This study proposes a robust solution to address the challenges posed by DGA-based botnets by developing an innovative machine learning-based model for domain name classification. The model leverages the light gradient boosting algorithm (LightGBM) and integrates n-gram features to enhance the detection of malicious DGA domains. This approach offers superior accuracy, adaptability, and efficiency in identifying and classifying anomalous domain names, achieving 96% precision when detecting true DGA domains. This system represents a significant advancement in cybersecurity and anomaly detection. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 194195919
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Botnet Detection: A System for Identifying DGA-based Botnets Using LightGBM.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Mohamad%2C+Mumtazimah%22">Mohamad, Mumtazimah</searchLink><relatesTo>1</relatesTo><i> mumtaz@unisza.edu.my</i><br /><searchLink fieldCode="AR" term="%22Hamid%2C+Nazirah+Abd%22">Hamid, Nazirah Abd</searchLink><relatesTo>1</relatesTo><i> nazirah@unisza.edu.my</i><br /><searchLink fieldCode="AR" term="%22Ghaleb%2C+Sanaa%22">Ghaleb, Sanaa</searchLink><relatesTo>1</relatesTo><i> sanaaabduljabbar@unisza.edu.my</i><br /><searchLink fieldCode="AR" term="%22Satar%2C+Siti+Dhalila+Mohd%22">Satar, Siti Dhalila Mohd</searchLink><relatesTo>1</relatesTo><i> sitidhalila@unisza.edu.my</i><br /><searchLink fieldCode="AR" term="%22Safei%2C+Suhailan%22">Safei, Suhailan</searchLink><relatesTo>2</relatesTo><i> suhailan@unisza.edu.my</i><br /><searchLink fieldCode="AR" term="%22Wan+Hamzah%2C+Wan+Mohd+Amir+Fazamin%22">Wan Hamzah, Wan Mohd Amir Fazamin</searchLink><relatesTo>1</relatesTo><i> amirfazamin@unisza.edu.my</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Applied+Mathematics%22">IAENG International Journal of Applied Mathematics</searchLink>. Jun2026, Vol. 56 Issue 6, p2291-2300. 10p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Botnets%22">Botnets</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Botnets using domain generation algorithms (DGAs) pose a major challenge to anomaly detection, as they generate large numbers of random domains to evade blacklists. Traditional methods struggle to classify these domains effectively due to their dynamic nature. This study proposes a robust solution to address the challenges posed by DGA-based botnets by developing an innovative machine learning-based model for domain name classification. The model leverages the light gradient boosting algorithm (LightGBM) and integrates n-gram features to enhance the detection of malicious DGA domains. This approach offers superior accuracy, adaptability, and efficiency in identifying and classifying anomalous domain names, achieving 96% precision when detecting true DGA domains. This system represents a significant advancement in cybersecurity and anomaly detection. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Applied Mathematics is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194195919
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 2291
    Subjects:
      – SubjectFull: Botnets
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Internet security
        Type: general
      – SubjectFull: Boosting algorithms
        Type: general
      – SubjectFull: Anomaly detection (Computer security)
        Type: general
    Titles:
      – TitleFull: Botnet Detection: A System for Identifying DGA-based Botnets Using LightGBM.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Mohamad, Mumtazimah
      – PersonEntity:
          Name:
            NameFull: Hamid, Nazirah Abd
      – PersonEntity:
          Name:
            NameFull: Ghaleb, Sanaa
      – PersonEntity:
          Name:
            NameFull: Satar, Siti Dhalila Mohd
      – PersonEntity:
          Name:
            NameFull: Safei, Suhailan
      – PersonEntity:
          Name:
            NameFull: Wan Hamzah, Wan Mohd Amir Fazamin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 19929978
          Numbering:
            – Type: volume
              Value: 56
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: IAENG International Journal of Applied Mathematics
              Type: main
ResultId 1