Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models
As the Internet of Things (IoT) landscape rapidly evolves, robust network security measures are imperative. In particular, Intrusion Detection Systems play a very important role in the preservation of an IoT environment from malicious activities. This paper provides a comprehensive performance compa...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10744011/ |
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| author | Najm Us Sama Saeed Ullah S. M. Ahsan Kazmi Manuel Mazzara |
| author_facet | Najm Us Sama Saeed Ullah S. M. Ahsan Kazmi Manuel Mazzara |
| author_sort | Najm Us Sama |
| collection | DOAJ |
| description | As the Internet of Things (IoT) landscape rapidly evolves, robust network security measures are imperative. In particular, Intrusion Detection Systems play a very important role in the preservation of an IoT environment from malicious activities. This paper provides a comprehensive performance comparison of various machine learning classifiers, including K-Nearest Neighbors, Gradient Boosting, XGBoost, Support Vector Machines, Random Forests, Decision Trees, and Extremely Randomized Trees, for intrusion detection in IoT networks. Comparative analysis shows that although all models did very well, the ensemble methods—GB, XGBoost, RF, and ERT—constantly performed better than others in F1-Score, recall, accuracy, and precision. Among them, ERT is turned out to be the most effective model for real-time attack detection on IoT devices, with an accuracy of 99.7% besides excellent precision and recall. XGBoost and RF also turn out to have high reliability and accuracy with F1-Scores of 0.95. These findings further underscore that ensemble methods outperform in intrusion detection for IoT networks and, thus, offer important insights to improve security within networks and protect critical IoT-based infrastructures from a variety of threats. |
| format | Article |
| id | doaj-art-7929dfabcbf74e918bf4ca5cef65d48f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7929dfabcbf74e918bf4ca5cef65d48f2025-01-15T00:03:07ZengIEEEIEEE Access2169-35362025-01-01138375839210.1109/ACCESS.2024.349183110744011Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble ModelsNajm Us Sama0https://orcid.org/0000-0002-1590-9562Saeed Ullah1https://orcid.org/0000-0001-8946-7640S. M. Ahsan Kazmi2Manuel Mazzara3https://orcid.org/0000-0002-3860-4948School of Computing, University of Derby, Derby, U.K.School of Computing, University of Derby, Derby, U.K.School of Computer Science and Creative Technologies, University of the West of England, Bristol, U.K.Institute of Software Development and Engineering, Innopolis University, Innopolis, RussiaAs the Internet of Things (IoT) landscape rapidly evolves, robust network security measures are imperative. In particular, Intrusion Detection Systems play a very important role in the preservation of an IoT environment from malicious activities. This paper provides a comprehensive performance comparison of various machine learning classifiers, including K-Nearest Neighbors, Gradient Boosting, XGBoost, Support Vector Machines, Random Forests, Decision Trees, and Extremely Randomized Trees, for intrusion detection in IoT networks. Comparative analysis shows that although all models did very well, the ensemble methods—GB, XGBoost, RF, and ERT—constantly performed better than others in F1-Score, recall, accuracy, and precision. Among them, ERT is turned out to be the most effective model for real-time attack detection on IoT devices, with an accuracy of 99.7% besides excellent precision and recall. XGBoost and RF also turn out to have high reliability and accuracy with F1-Scores of 0.95. These findings further underscore that ensemble methods outperform in intrusion detection for IoT networks and, thus, offer important insights to improve security within networks and protect critical IoT-based infrastructures from a variety of threats.https://ieeexplore.ieee.org/document/10744011/AccuracyInternet of Things (IoT)intrusion detection systems (IDS)machine learning classifiers |
| spellingShingle | Najm Us Sama Saeed Ullah S. M. Ahsan Kazmi Manuel Mazzara Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models IEEE Access Accuracy Internet of Things (IoT) intrusion detection systems (IDS) machine learning classifiers |
| title | Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models |
| title_full | Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models |
| title_fullStr | Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models |
| title_full_unstemmed | Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models |
| title_short | Cutting-Edge Intrusion Detection in IoT Networks: A Focus on Ensemble Models |
| title_sort | cutting edge intrusion detection in iot networks a focus on ensemble models |
| topic | Accuracy Internet of Things (IoT) intrusion detection systems (IDS) machine learning classifiers |
| url | https://ieeexplore.ieee.org/document/10744011/ |
| work_keys_str_mv | AT najmussama cuttingedgeintrusiondetectioniniotnetworksafocusonensemblemodels AT saeedullah cuttingedgeintrusiondetectioniniotnetworksafocusonensemblemodels AT smahsankazmi cuttingedgeintrusiondetectioniniotnetworksafocusonensemblemodels AT manuelmazzara cuttingedgeintrusiondetectioniniotnetworksafocusonensemblemodels |