Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models
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 capabilitie...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
|
| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2321381 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846118834688229376 |
|---|---|
| author | Ayoob Almotairi Samer Atawneh Osama A. Khashan Nour M. Khafajah |
| author_facet | Ayoob Almotairi Samer Atawneh Osama A. Khashan Nour M. Khafajah |
| author_sort | Ayoob Almotairi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b541f88bf390464e95d26ffc4739fe6b |
| institution | Kabale University |
| issn | 2164-2583 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-b541f88bf390464e95d26ffc4739fe6b2024-12-17T09:06:12ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832024-12-0112110.1080/21642583.2024.2321381Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble modelsAyoob Almotairi0Samer Atawneh1Osama A. Khashan2Nour M. Khafajah3College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaCollege of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaResearch and Innovation Centers, Rabdan Academy, Abu Dhabi, United Arab EmiratesMEU Research Unit, Middle East University, Amman, JordanInternet 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.https://www.tandfonline.com/doi/10.1080/21642583.2024.2321381Internet of Things (IoT)intrusion detectionmachine learning (ML)feature selectionensemble learning |
| spellingShingle | Ayoob Almotairi Samer Atawneh Osama A. Khashan Nour M. Khafajah Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models Systems Science & Control Engineering Internet of Things (IoT) intrusion detection machine learning (ML) feature selection ensemble learning |
| title | Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models |
| title_full | Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models |
| title_fullStr | Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models |
| title_full_unstemmed | Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models |
| title_short | Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models |
| title_sort | enhancing intrusion detection in iot networks using machine learning based feature selection and ensemble models |
| topic | Internet of Things (IoT) intrusion detection machine learning (ML) feature selection ensemble learning |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2024.2321381 |
| work_keys_str_mv | AT ayoobalmotairi enhancingintrusiondetectioniniotnetworksusingmachinelearningbasedfeatureselectionandensemblemodels AT sameratawneh enhancingintrusiondetectioniniotnetworksusingmachinelearningbasedfeatureselectionandensemblemodels AT osamaakhashan enhancingintrusiondetectioniniotnetworksusingmachinelearningbasedfeatureselectionandensemblemodels AT nourmkhafajah enhancingintrusiondetectioniniotnetworksusingmachinelearningbasedfeatureselectionandensemblemodels |