Dimensional Feature Reduction for Detecting Botnet Activities

Muhammad Aidiel Rachman Putra*, Tohari Ahmad, Dandy Pramana Hostiadi

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Rising number of devices linked to the internet has made computer network security crucial. Those devices may be compromised, forming botnets, one of the most severe threats to network security due to their unique characteristics. An in-depth analysis of various processes, including feature extraction, is required to develop a botnet detection model with reliable performance. In this system, feature extraction is one of feature engineering, which is part of the data pre-processing. To find the best approach, we analyze the impact of feature extraction using dimensional reduction with four techniques: Principal Component Analysis, Truncate Singular Value Decomposition, Factor Analysis, and Fast Independent Component Analysis. The feature extraction results are brought to the classification stage to analyze their impact using several machine learning algorithms such as k-NN, Decision Tree, Random Forest, Naive Bayes, and Logistic Regression. Using the CTU-13, NCC-1, and NCC-2 datasets, it is found that dimensional reduction is suitable with k-NN but not recommended for a tree-based machine learning algorithm.

Original languageEnglish
Title of host publication25th International Conference on Advanced Communications Technology
Subtitle of host publicationNew Cyber Security Risks for Enterprise Amidst COVID-19 Pandemic!!, ICACT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages43-48
Number of pages6
ISBN (Electronic)9791188428106
DOIs
Publication statusPublished - 2023
Event25th International Conference on Advanced Communications Technology, ICACT 2023 - Pyeongchang, Korea, Republic of
Duration: 19 Feb 202322 Feb 2023

Publication series

NameInternational Conference on Advanced Communication Technology, ICACT
Volume2023-February
ISSN (Print)1738-9445

Conference

Conference25th International Conference on Advanced Communications Technology, ICACT 2023
Country/TerritoryKorea, Republic of
CityPyeongchang
Period19/02/2322/02/23

Keywords

  • botnet detection
  • dimensional reduction
  • intrusion detection system
  • network infrastructure
  • network security

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