Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques
Recent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in a rise in poli...
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MDPI AG
2025-02-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/3/82 |
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| author | Ghalia Nassreddine Mohamad Nassereddine Obada Al-Khatib |
| author_facet | Ghalia Nassreddine Mohamad Nassereddine Obada Al-Khatib |
| author_sort | Ghalia Nassreddine |
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| description | Recent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in a rise in policy infractions impacting information security. Finding intruders immediately is a critical component of maintaining network security. The intrusion detection system is useful for network security because it can quickly identify threats and give alarms. In this paper, a new approach for network intrusion detection was proposed. Combining the results of machine learning models like the random forest, decision tree, k-nearest neighbors, and XGBoost with logistic regression as a meta-model is what this method is based on. For the feature selection technique, the proposed approach creates an advanced method that combines the correlation-based feature selection with an embedded technique based on XGBoost. For handling the challenge of an imbalanced dataset, a SMOTE-TOMEK technique is used. The suggested algorithm is tested on the NSL-KDD and CIC-IDS datasets. It shows a high performance with an accuracy of 99.99% for both datasets. These results prove the effectiveness of the proposed approach. |
| format | Article |
| id | doaj-art-fee76c5869f940328711821398ca545f |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-fee76c5869f940328711821398ca545f2025-08-20T03:43:33ZengMDPI AGComputers2073-431X2025-02-011438210.3390/computers14030082Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection TechniquesGhalia Nassreddine0Mohamad Nassereddine1Obada Al-Khatib2Computer and Information Systems Department, Rafik Hariri University, Damour-Chouf 2010, LebanonSchool of Engineering, University of Wollongong in Dubai, Knowledge Village, Dubai P.O. Box 20183, United Arab EmiratesSchool of Engineering, University of Wollongong in Dubai, Knowledge Village, Dubai P.O. Box 20183, United Arab EmiratesRecent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in a rise in policy infractions impacting information security. Finding intruders immediately is a critical component of maintaining network security. The intrusion detection system is useful for network security because it can quickly identify threats and give alarms. In this paper, a new approach for network intrusion detection was proposed. Combining the results of machine learning models like the random forest, decision tree, k-nearest neighbors, and XGBoost with logistic regression as a meta-model is what this method is based on. For the feature selection technique, the proposed approach creates an advanced method that combines the correlation-based feature selection with an embedded technique based on XGBoost. For handling the challenge of an imbalanced dataset, a SMOTE-TOMEK technique is used. The suggested algorithm is tested on the NSL-KDD and CIC-IDS datasets. It shows a high performance with an accuracy of 99.99% for both datasets. These results prove the effectiveness of the proposed approach.https://www.mdpi.com/2073-431X/14/3/82network intrusion detectionmachine learningfeature selectionimbalanced datasetstacking techniqueembedded feature selection technique |
| spellingShingle | Ghalia Nassreddine Mohamad Nassereddine Obada Al-Khatib Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques Computers network intrusion detection machine learning feature selection imbalanced dataset stacking technique embedded feature selection technique |
| title | Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques |
| title_full | Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques |
| title_fullStr | Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques |
| title_full_unstemmed | Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques |
| title_short | Ensemble Learning for Network Intrusion Detection Based on Correlation and Embedded Feature Selection Techniques |
| title_sort | ensemble learning for network intrusion detection based on correlation and embedded feature selection techniques |
| topic | network intrusion detection machine learning feature selection imbalanced dataset stacking technique embedded feature selection technique |
| url | https://www.mdpi.com/2073-431X/14/3/82 |
| work_keys_str_mv | AT ghalianassreddine ensemblelearningfornetworkintrusiondetectionbasedoncorrelationandembeddedfeatureselectiontechniques AT mohamadnassereddine ensemblelearningfornetworkintrusiondetectionbasedoncorrelationandembeddedfeatureselectiontechniques AT obadaalkhatib ensemblelearningfornetworkintrusiondetectionbasedoncorrelationandembeddedfeatureselectiontechniques |