Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers
IntroductionCloud computing has revolutionized how organizations manage their infrastructure by providing scalable, on-demand services. However, the dispersed and open nature of cloud systems exposes them to a wide spectrum of cyberattacks. Machine learning provides dynamic options for detecting kno...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Computer Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1623375/full |
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| author | Khawla Ali Maodah Sharaf Alhomdy Fursan Thabit |
| author_facet | Khawla Ali Maodah Sharaf Alhomdy Fursan Thabit |
| author_sort | Khawla Ali Maodah |
| collection | DOAJ |
| description | IntroductionCloud computing has revolutionized how organizations manage their infrastructure by providing scalable, on-demand services. However, the dispersed and open nature of cloud systems exposes them to a wide spectrum of cyberattacks. Machine learning provides dynamic options for detecting known and unknown assaults, whereas typical intrusion detection systems that depend on signature or rule-based techniques find it difficult to adjust to complex cyber threats.MethodsThis study compares the efficacy of an ensemble approach (Voting Hard and Stacking) for intrusion detection in cloud environments with individual machine learning classifiers, such as Random Forest, Decision Tree, Gradient Boosting, XGBoost, Naive Bayes, Support Vector Machine, and Logistic Regression. The study uses the NSL-KDD dataset.ResultsThe results show show that while standalone models perform well, the ensemble technique offers better accuracy (almost 100%) and resilience across precision, recall, and F1-score measures. Furthermore, it is shown via feature selection methods (Random Forest, Gain Information, and Manual Selection) that the ensemble model performs consistently even when feature sets are smaller.DiscussionThese findings highlight how both individual and group Machine learning approaches may be used to improve Intrusion detection systems for cloud infrastructures, providing implementation flexibility according to threat landscapes and computing limitations. |
| format | Article |
| id | doaj-art-97868c326a7244cd9e3743ed989cd573 |
| institution | Kabale University |
| issn | 2624-9898 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj-art-97868c326a7244cd9e3743ed989cd5732025-08-20T05:32:52ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982025-08-01710.3389/fcomp.2025.16233751623375Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiersKhawla Ali Maodah0Sharaf Alhomdy1Fursan Thabit2Department of Information Technology, Faculty of Computer and Information Technology, Sana’a University, Sana’a, YemenDepartment of Information Technology, Faculty of Computer and Information Technology, Sana’a University, Sana’a, YemenDepartment of Computer Engineering, Faculty of Engineering, Ege University, Bornova, TürkiyeIntroductionCloud computing has revolutionized how organizations manage their infrastructure by providing scalable, on-demand services. However, the dispersed and open nature of cloud systems exposes them to a wide spectrum of cyberattacks. Machine learning provides dynamic options for detecting known and unknown assaults, whereas typical intrusion detection systems that depend on signature or rule-based techniques find it difficult to adjust to complex cyber threats.MethodsThis study compares the efficacy of an ensemble approach (Voting Hard and Stacking) for intrusion detection in cloud environments with individual machine learning classifiers, such as Random Forest, Decision Tree, Gradient Boosting, XGBoost, Naive Bayes, Support Vector Machine, and Logistic Regression. The study uses the NSL-KDD dataset.ResultsThe results show show that while standalone models perform well, the ensemble technique offers better accuracy (almost 100%) and resilience across precision, recall, and F1-score measures. Furthermore, it is shown via feature selection methods (Random Forest, Gain Information, and Manual Selection) that the ensemble model performs consistently even when feature sets are smaller.DiscussionThese findings highlight how both individual and group Machine learning approaches may be used to improve Intrusion detection systems for cloud infrastructures, providing implementation flexibility according to threat landscapes and computing limitations.https://www.frontiersin.org/articles/10.3389/fcomp.2025.1623375/fullcloud computingmachine learningvotingstackingintrusion detection systemNSL-KDD dataset |
| spellingShingle | Khawla Ali Maodah Sharaf Alhomdy Fursan Thabit Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers Frontiers in Computer Science cloud computing machine learning voting stacking intrusion detection system NSL-KDD dataset |
| title | Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers |
| title_full | Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers |
| title_fullStr | Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers |
| title_full_unstemmed | Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers |
| title_short | Detecting intrusions in cloud-based ensembles: evaluating voting and stacking methods with machine learning classifiers |
| title_sort | detecting intrusions in cloud based ensembles evaluating voting and stacking methods with machine learning classifiers |
| topic | cloud computing machine learning voting stacking intrusion detection system NSL-KDD dataset |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2025.1623375/full |
| work_keys_str_mv | AT khawlaalimaodah detectingintrusionsincloudbasedensemblesevaluatingvotingandstackingmethodswithmachinelearningclassifiers AT sharafalhomdy detectingintrusionsincloudbasedensemblesevaluatingvotingandstackingmethodswithmachinelearningclassifiers AT fursanthabit detectingintrusionsincloudbasedensemblesevaluatingvotingandstackingmethodswithmachinelearningclassifiers |