Analysis of Weight-Based Voting Classifier for Intrusion Detection System

Miftahul Hasanah, Rizqy Ahsana Putri, Muhammad Aidie, Rachman Putra, Tohari Ahmad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The evolution of technology and the internet has accelerated the pace of communication and information exchange. Despite technological advancements, the significant weakness lies in the persistent threat of cybercrime, manifesting in various forms like malware, phishing, and ransomware. To solve the cybercrime problems, this research aims to create an intrusion detection system model using a novel framework. In general, the proposed method consists of 3 stages: Data preprocessing, feature selection using ANOVA F-value combined with cross validation, and classification using weight-based voting classifier. Some machine learning methods used in the weight-based voting classifier are random forest, K-nearest neighbour, and logistic regression. The experiment results show that weight order and weight combination affect the detection performance. The proposed method produces an excellent precision value of 98.66%, higher than the single voting classifier.

Original languageEnglish
Pages (from-to)190-200
Number of pages11
JournalInternational Journal of Intelligent Engineering and Systems
Issue number2
Publication statusPublished - 2024


  • IDS
  • Information security
  • National security
  • Network security
  • UNSW-NB15
  • Weight-based voting classifier


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