LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems
<p dir="ltr">The rapid proliferation of real-time Internet of Things (IoT) devices has increased the need for efficient and accurate security mechanisms. Real-time IoT devices are highly vulnerable to cyberattacks due to their continuous connectivity and limited security mechanisms....
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2025
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| _version_ | 1864513533195059200 |
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| author | Muhammad Usama Tanveer (22225360) |
| author2 | Kashif Munir (6182237) Madiha Amjad (20036518) Hasan J. Alyamani (22502435) Amine Bermak (1895947) Atiq Ur Rehman (22502438) |
| author2_role | author author author author author |
| author_facet | Muhammad Usama Tanveer (22225360) Kashif Munir (6182237) Madiha Amjad (20036518) Hasan J. Alyamani (22502435) Amine Bermak (1895947) Atiq Ur Rehman (22502438) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Usama Tanveer (22225360) Kashif Munir (6182237) Madiha Amjad (20036518) Hasan J. Alyamani (22502435) Amine Bermak (1895947) Atiq Ur Rehman (22502438) |
| dc.date.none.fl_str_mv | 2025-06-17T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3576218 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/LightEnsemble-Guard_An_Optimized_Ensemble_Learning_Framework_for_Securing_Resource-Constrained_IoT_Systems/30540839 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning LightEnsemble-guard ensemble learning cybersecurity attack detection machine learning |
| dc.title.none.fl_str_mv | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The rapid proliferation of real-time Internet of Things (IoT) devices has increased the need for efficient and accurate security mechanisms. Real-time IoT devices are highly vulnerable to cyberattacks due to their continuous connectivity and limited security mechanisms. In this work, we propose LightEnsemble-Guard, an ensemble learning-based approach specifically designed for resource-constrained IoT environments. Our method achieves a high detection accuracy of 99.55%, with strong precision, recall, and F1-score, while maintaining a low computational cost of just 22.23 seconds. To ensure robustness, we conducted 5-fold cross-validation and ROC curve evaluations, confirming the model’s reliability and generalizability. LightEnsemble-Guard integrates three lightweight classifiers LightGBM, XGBoost, and Extra Trees using a majority voting mechanism to improve detection performance on highly imbalanced datasets without burdening system resources. This ensemble strategy ensures an optimal balance between detection performance and computational resource utilization, making it well-suited for IoT networks, where processing power and memory are limited. The results highlight LightEnsemble-Guard as an effective, scalable and lightweight solution for real-time IoT security, significantly outperforming traditional models in both accuracy and computational efficiency.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3576218" target="_blank">https://dx.doi.org/10.1109/access.2025.3576218</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e726b22c272954dd6ce29701c0eccfdd |
| identifier_str_mv | 10.1109/access.2025.3576218 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30540839 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT SystemsMuhammad Usama Tanveer (22225360)Kashif Munir (6182237)Madiha Amjad (20036518)Hasan J. Alyamani (22502435)Amine Bermak (1895947)Atiq Ur Rehman (22502438)Information and computing sciencesArtificial intelligenceCybersecurity and privacyMachine learningLightEnsemble-guardensemble learningcybersecurityattack detectionmachine learning<p dir="ltr">The rapid proliferation of real-time Internet of Things (IoT) devices has increased the need for efficient and accurate security mechanisms. Real-time IoT devices are highly vulnerable to cyberattacks due to their continuous connectivity and limited security mechanisms. In this work, we propose LightEnsemble-Guard, an ensemble learning-based approach specifically designed for resource-constrained IoT environments. Our method achieves a high detection accuracy of 99.55%, with strong precision, recall, and F1-score, while maintaining a low computational cost of just 22.23 seconds. To ensure robustness, we conducted 5-fold cross-validation and ROC curve evaluations, confirming the model’s reliability and generalizability. LightEnsemble-Guard integrates three lightweight classifiers LightGBM, XGBoost, and Extra Trees using a majority voting mechanism to improve detection performance on highly imbalanced datasets without burdening system resources. This ensemble strategy ensures an optimal balance between detection performance and computational resource utilization, making it well-suited for IoT networks, where processing power and memory are limited. The results highlight LightEnsemble-Guard as an effective, scalable and lightweight solution for real-time IoT security, significantly outperforming traditional models in both accuracy and computational efficiency.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3576218" target="_blank">https://dx.doi.org/10.1109/access.2025.3576218</a></p>2025-06-17T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3576218https://figshare.com/articles/journal_contribution/LightEnsemble-Guard_An_Optimized_Ensemble_Learning_Framework_for_Securing_Resource-Constrained_IoT_Systems/30540839CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305408392025-06-17T12:00:00Z |
| spellingShingle | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems Muhammad Usama Tanveer (22225360) Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning LightEnsemble-guard ensemble learning cybersecurity attack detection machine learning |
| status_str | publishedVersion |
| title | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems |
| title_full | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems |
| title_fullStr | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems |
| title_full_unstemmed | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems |
| title_short | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems |
| title_sort | LightEnsemble-Guard: An Optimized Ensemble Learning Framework for Securing Resource-Constrained IoT Systems |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning LightEnsemble-guard ensemble learning cybersecurity attack detection machine learning |