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This study aims to evaluate the performance of various machine learning models in intrusion detection systems, utilizing a novel feature selection technique.

January 4, 2022CybersecurityOpen Access

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

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Authors

RDRaisa Abedin DishaBangladesh University of Professionals SWSajjad WaheedMawlana Bhashani Science and Technology University

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Implication

Randomized trial evaluates machine learning models for intrusion detection, suggesting Gini impurity improves feature selection effectiveness.

Key Points

This study aims to evaluate the performance of various machine learning models in intrusion detection systems, utilizing a novel feature selection technique.
Analyzed machine learning models including Decision Tree, Gradient Boosting Tree, and others on UNSW-NB 15 and Network TON_IoT datasets.
Used Gini Impurity-based Weighted Random Forest for optimal feature selection to address high dimensionality issues in datasets.
Selected 20 features from UNSW-NB 15 and 10 features from Network TON_IoT based on importance scores.
Decision Tree model achieved superior performance with the feature selection technique compared to other models.
GIWRF-DT model outperformed existing methods in the literature, specifically in terms of F1 score.

Cite This Study

Disha et al. (2022) studied this question.

synapsesocial.com/papers/69dc4bd44f901957bec1016f https://doi.org/10.1186/s42400-021-00103-8

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Implication

Randomized trial evaluates machine learning models for intrusion detection, suggesting Gini impurity improves feature selection effectiveness.

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Discussion

Member takes

Implication

Key Points

Cite This Study

Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique

Discussion

Member takes

Implication

Key Points

Cite This Study

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