Smart City Gnosys
Smart city article details
| Title | Enhancing Intrusion Detection In Iot Networks Using Machine Learning-Based Feature Selection And Ensemble Models |
|---|---|
| ID_Doc | 23837 |
| Authors | Almotairi A.; Atawneh S.; Khashan O.A.; Khafajah N.M. |
| Year | 2024 |
| Published | Systems Science and Control Engineering, 12, 1 |
| DOI | http://dx.doi.org/10.1080/21642583.2024.2321381 |
| Abstract | Internet of Things (IoT) technology has evolved significantly, transitioning from personal devices to powering smart cities and global deployments across diverse industries. However, security challenges arise due to diverse devices using various protocols and having limited computational capabilities, leading to vulnerabilities and potential intrusions in IoT networks. This paper addresses the challenge of intrusion detection in IoT by introducing a heterogeneous machine learning-based stack classifier model for IoT data. The model employs feature selection and ensemble modelling to investigate and enhance key classification metrics for intrusion detection of IoT data. This approach comprises two core components: the utilization of the K-Best algorithm for feature selection, extracting the top 15 critical features and the construction of an ensemble model incorporating various traditional machine learning models. The integration of these components harnesses information from selected features and leverages the collective strength of individual models to enhance classification performance. Using the ‘Ton IoT dataset,’ our experiments compare the ensemble model with individual ones. This research aims to improve key classification metrics for IoT intrusion detection, focusing on accuracy, precision, recall and F1 score. Through rigorous experimentation and comparisons, the proposed ensemble approach showcases exceptional performance, providing a robust solution to fortify IoT network security. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. |
| Author Keywords | ensemble learning; feature selection; Internet of Things (IoT); intrusion detection; machine learning (ML) |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
36055  | 0.938 | Ngo V.-D.; Vuong T.-C.; Van Luong T.; Tran H. | Machine Learning-Based Intrusion Detection: Feature Selection Versus Feature Extraction | Cluster Computing, 27, 3 (2024) |
| 3003 | 0.931 | Gopalakrishnan B.; Purusothaman P. | A New Design Of Intrusion Detection In Iot Sector Using Optimal Feature Selection And High Ranking-Based Ensemble Learning Model | Peer-to-Peer Networking and Applications, 15, 5 (2022) |
| 22935 | 0.925 | Merlin R.T.; Ravi R. | Empowering Smart City Iot Network Intrusion Detection With Advanced Ensemble Learning-Based Feature Selection | International Journal of Electrical and Electronics Research, 12, 2 (2024) |
| 33346 | 0.924 | Berhili M.; Chaieb O.; Benabdellah M. | Intrusion Detection Systems In Iot Based On Machine Learning: A State Of The Art | Procedia Computer Science, 251 (2024) |
| 2483 | 0.917 | Nepolo E.; Ngxande M.; Zodi G.-A.L. | A Machine Learning-Based Performance Analysis Of Feature Selection Methods For Anomaly Detection For Iot Network Security | Learning and Analytics in Intelligent Systems, 43 (2025) |
| 5689 | 0.913 | Srivastav D.; Srivastava P. | A Two-Tier Hybrid Ensemble Learning Pipeline For Intrusion Detection Systems In Iot Networks | Journal of Ambient Intelligence and Humanized Computing, 14, 4 (2023) |
| 57650 | 0.912 | Hazman C.; Guezzaz A.; Benkirane S.; Azrour M. | Toward An Intrusion Detection Model For Iot-Based Smart Environments | Multimedia Tools and Applications, 83, 22 (2024) |
| 831 | 0.91 | Vuong T.-C.; Tran H.; Trang M.X.; Ngo V.-D.; Luong T.V. | A Comparison Of Feature Selection And Feature Extraction In Network Intrusion Detection Systems | Proceedings of 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2022 (2022) |
| 8068 | 0.909 | Hariprasad S.; Deepa T. | An Ensemble Intrusion Detection System Based On Acute Feature Selection | Multimedia Tools and Applications, 83, 3 (2024) |
| 37199 | 0.901 | Al-Ambusaidi M.; Yinjun Z.; Muhammad Y.; Yahya A. | Ml-Ids: An Efficient Ml-Enabled Intrusion Detection System For Securing Iot Networks And Applications | Soft Computing, 28, 2 (2024) |