Analyzing Machine Learning-based Feature Selection for Botnet Detection

Winda Ayu Safitri, Tohari Ahmad, Dandy Pramana Hostiadi

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

2 Citations (Scopus)

Abstract

In this cyber era, the number of cybercrime problems grows significantly, impacting network communication security. Some factors have been identified, such as malware. It is a malicious code attack that is harmful. On the other hand, a botnet can exploit malware to threaten whole computer networks. Therefore, it needs to be handled appropriately. Several botnet activity detection models have been developed using a classification approach in previous studies. However, it has not been analyzed about selecting features to be used in the learning process of the classification algorithm. In fact, the number and selection of features implemented can affect the detection accuracy of the classification algorithm. This paper proposes an analysis technique for determining the number and selection of features developed based on previous research. It aims to obtain the analysis of using features. The experiment has been conducted using several classification algorithms, namely Decision tree, k-NN, Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The results show that taking a certain number of features increases the detection accuracy. Compared with previous studies, the results obtained show that the average detection accuracy of 98.34% using four features has the highest value from the previous study, 97.46% using 11 features. These results indicate that the selection of the correct number and features affects the performance of the botnet detection model.

Original languageEnglish
Title of host publication2022 1st International Conference on Information System and Information Technology, ICISIT 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages386-391
Number of pages6
ISBN (Electronic)9781665402002
DOIs
Publication statusPublished - 2022
Event1st International Conference on Information System and Information Technology, ICISIT 2022 - Virtual, Online, Indonesia
Duration: 27 Jul 202228 Jul 2022

Publication series

Name2022 1st International Conference on Information System and Information Technology, ICISIT 2022

Conference

Conference1st International Conference on Information System and Information Technology, ICISIT 2022
Country/TerritoryIndonesia
CityVirtual, Online
Period27/07/2228/07/22

Keywords

  • botnet detection
  • feature selection
  • infrastructure
  • intrusion detection system
  • network security

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