Abstract

Botnets are a type of malware that threatens network security. One of the frequently encountered botnet threats is SPAM. Many studies focus on building detection models to classify botnet and non-botnet activities in network flows. Thus, research that can specifically differentiate SPAM from botnet activities is quite challenging. This paper proposes a model to detect SPAM botnet activity in network traffic using two-stack decision tree algorithms. The first stack of the model focuses on classifying network traffic into botnet and normal activity classes. Meanwhile, the second stack classifies botnet activity into two types: spam botnets and non-spam botnets. The experimental results show that the proposed model performs better than the Decision Tree model, which detects three activity classes directly. Performance evaluation of the proposed model succeeded in getting a value of 97.19% accuracy, 97.13% precision, 97.19% recall. and 97.12%F'1-score.

Original languageEnglish
Title of host publication2023 IEEE International Conference of Computer Science and Information Technology
Subtitle of host publicationThe Role of Artificial Intelligence Technology in Human and Computer Interactions in the Industrial Era 5.0, ICOSNIKOM 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350360752
DOIs
Publication statusPublished - 2023
Event7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023 - Hybrid, Binjia, Indonesia
Duration: 10 Nov 202311 Nov 2023

Publication series

Name2023 IEEE International Conference of Computer Science and Information Technology: The Role of Artificial Intelligence Technology in Human and Computer Interactions in the Industrial Era 5.0, ICOSNIKOM 2023

Conference

Conference7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023
Country/TerritoryIndonesia
CityHybrid, Binjia
Period10/11/2311/11/23

Keywords

  • botnet detection
  • machine learning
  • network infrastructure
  • network security
  • spam

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