Ensemble learning-based IDS for sensors telemetry data in IoT networks
The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocol...
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022493?viewType=HTML |
| _version_ | 1798040488834498560 |
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| author | Naila Naz Muazzam A Khan Suliman A. Alsuhibany Muhammad Diyan Zhiyuan Tan Muhammad Almas Khan Jawad Ahmad |
| author_facet | Naila Naz Muazzam A Khan Suliman A. Alsuhibany Muhammad Diyan Zhiyuan Tan Muhammad Almas Khan Jawad Ahmad |
| author_sort | Naila Naz |
| collection | DOAJ |
| description | The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocols such as CoAP, MQTT, DDS, etc. Study shows that these protocols are vulnerable to attack and prove a significant threat to IoT telemetry data. Within a network, IoT devices are interdependent, and the behaviour of one device depends on the data coming from another device. An intruder exploits vulnerabilities of a device's interdependent feature and can alter the telemetry data to indirectly control the behaviour of other dependent devices in a network. Therefore, securing IoT devices have become a significant concern in IoT networks. The research community often proposes intrusion Detection Systems (IDS) using different techniques. One of the most adopted techniques is machine learning (ML) based intrusion detection. This study suggests a stacking-based ensemble model makes IoT devices more intelligent for detecting unusual behaviour in IoT networks. The TON-IoT (2020) dataset is used to assess the effectiveness of the proposed model. The proposed model achieves significant improvements in accuracy and other evaluation measures in binary and multi-class classification scenarios for most of the sensors compared to traditional ML algorithms and other ensemble techniques. |
| first_indexed | 2024-04-11T22:08:19Z |
| format | Article |
| id | doaj.art-b46003d4215a4a029afb7c46a9386652 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-11T22:08:19Z |
| publishDate | 2022-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-b46003d4215a4a029afb7c46a93866522022-12-22T04:00:38ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-07-011910105501058010.3934/mbe.2022493Ensemble learning-based IDS for sensors telemetry data in IoT networksNaila Naz0Muazzam A Khan1Suliman A. Alsuhibany2Muhammad Diyan3Zhiyuan Tan4Muhammad Almas Khan5Jawad Ahmad61. Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan1. Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan2. Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia3. School of Physics and Astronomy, University of Glasgow, United Kingdom4. School of Computing, Edinburgh Napier University, United Kingdom1. Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan4. School of Computing, Edinburgh Napier University, United KingdomThe Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with other devices using different communication protocols such as CoAP, MQTT, DDS, etc. Study shows that these protocols are vulnerable to attack and prove a significant threat to IoT telemetry data. Within a network, IoT devices are interdependent, and the behaviour of one device depends on the data coming from another device. An intruder exploits vulnerabilities of a device's interdependent feature and can alter the telemetry data to indirectly control the behaviour of other dependent devices in a network. Therefore, securing IoT devices have become a significant concern in IoT networks. The research community often proposes intrusion Detection Systems (IDS) using different techniques. One of the most adopted techniques is machine learning (ML) based intrusion detection. This study suggests a stacking-based ensemble model makes IoT devices more intelligent for detecting unusual behaviour in IoT networks. The TON-IoT (2020) dataset is used to assess the effectiveness of the proposed model. The proposed model achieves significant improvements in accuracy and other evaluation measures in binary and multi-class classification scenarios for most of the sensors compared to traditional ML algorithms and other ensemble techniques.https://www.aimspress.com/article/doi/10.3934/mbe.2022493?viewType=HTMLensemble learningintrusion detectioniotsensors securityton-iotbagging |
| spellingShingle | Naila Naz Muazzam A Khan Suliman A. Alsuhibany Muhammad Diyan Zhiyuan Tan Muhammad Almas Khan Jawad Ahmad Ensemble learning-based IDS for sensors telemetry data in IoT networks Mathematical Biosciences and Engineering ensemble learning intrusion detection iot sensors security ton-iot bagging |
| title | Ensemble learning-based IDS for sensors telemetry data in IoT networks |
| title_full | Ensemble learning-based IDS for sensors telemetry data in IoT networks |
| title_fullStr | Ensemble learning-based IDS for sensors telemetry data in IoT networks |
| title_full_unstemmed | Ensemble learning-based IDS for sensors telemetry data in IoT networks |
| title_short | Ensemble learning-based IDS for sensors telemetry data in IoT networks |
| title_sort | ensemble learning based ids for sensors telemetry data in iot networks |
| topic | ensemble learning intrusion detection iot sensors security ton-iot bagging |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022493?viewType=HTML |
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