Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques DOI Creative Commons
Ghalia Nassreddine, Mohamad Nassereddine, Obada Al-Khatib

et al.

Computers, Journal Year: 2025, Volume and Issue: 14(3), P. 82 - 82

Published: Feb. 25, 2025

Recent advancements across various sectors have resulted in a significant increase the utilization of smart gadgets. This augmentation has an expansion network and devices linked to it. Nevertheless, development concurrently rise policy infractions impacting information security. Finding intruders immediately is critical component maintaining The intrusion detection system useful for security because it can quickly identify threats give alarms. In this paper, new approach was proposed. Combining results machine learning models like random forest, decision tree, k-nearest neighbors, XGBoost with logistic regression as meta-model what method based on. For feature selection technique, proposed creates advanced that combines correlation-based embedded technique on XGBoost. handling challenge imbalanced dataset, SMOTE-TOMEK used. suggested algorithm tested NSL-KDD CIC-IDS datasets. It shows high performance accuracy 99.99% both These prove effectiveness approach.

Language: Английский

Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques DOI Creative Commons
Ghalia Nassreddine, Mohamad Nassereddine, Obada Al-Khatib

et al.

Computers, Journal Year: 2025, Volume and Issue: 14(3), P. 82 - 82

Published: Feb. 25, 2025

Recent advancements across various sectors have resulted in a significant increase the utilization of smart gadgets. This augmentation has an expansion network and devices linked to it. Nevertheless, development concurrently rise policy infractions impacting information security. Finding intruders immediately is critical component maintaining The intrusion detection system useful for security because it can quickly identify threats give alarms. In this paper, new approach was proposed. Combining results machine learning models like random forest, decision tree, k-nearest neighbors, XGBoost with logistic regression as meta-model what method based on. For feature selection technique, proposed creates advanced that combines correlation-based embedded technique on XGBoost. handling challenge imbalanced dataset, SMOTE-TOMEK used. suggested algorithm tested NSL-KDD CIC-IDS datasets. It shows high performance accuracy 99.99% both These prove effectiveness approach.

Language: Английский

Citations

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