Botnet Classification using Machine Learning

Man Ni, Gabriela Mogos*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Botnet is one of the cyber security threats which may cause big damages. This research uses Machine Learning methods to classify and then detect botnet attacks. The CTU-13 dataset is used, and 90,962 records are trained in a 3-layer Deep Learning sequential model. The result has 99% accuracy and the value of recall, precision and F1 score is 99% as well.

Original languageEnglish
Title of host publicationProceedings - International SoC Design Conference 2024, ISOCC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages197-198
Number of pages2
ISBN (Electronic)9798350377088
DOIs
Publication statusPublished - 2024
Event21st International System-on-Chip Design Conference, ISOCC 2024 - Sapporo, Japan
Duration: 19 Aug 202422 Aug 2024

Publication series

NameProceedings - International SoC Design Conference 2024, ISOCC 2024

Conference

Conference21st International System-on-Chip Design Conference, ISOCC 2024
Country/TerritoryJapan
CitySapporo
Period19/08/2422/08/24

Keywords

  • binary classification
  • Botnet
  • computer security
  • deep learning
  • machine learning

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