Naila Naz
Ensemble learning-based IDS for sensors telemetry data in IoT networks
Naz, Naila; Khan, Muazzam A; Alsuhibany, Suliman A.; Diyan, Muhammad; Tan, Zhiyuan; Khan, Muhammad Almas; Ahmad, Jawad
Authors
Muazzam A Khan
Suliman A. Alsuhibany
Muhammad Diyan
Dr Thomas Tan Z.Tan@napier.ac.uk
Associate Professor
Muhammad Almas Khan
Dr Jawad Ahmad J.Ahmad@napier.ac.uk
Visiting Lecturer
Abstract
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.
Citation
Naz, N., Khan, M. A., Alsuhibany, S. A., Diyan, M., Tan, Z., Khan, M. A., & Ahmad, J. (2022). Ensemble learning-based IDS for sensors telemetry data in IoT networks. Mathematical Biosciences and Engineering, 19(10), 10550-10580. https://doi.org/10.3934/mbe.2022493
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 4, 2022 |
Online Publication Date | Jul 25, 2022 |
Publication Date | Jul 25, 2022 |
Deposit Date | Aug 7, 2022 |
Publicly Available Date | Aug 8, 2022 |
Journal | Mathematical Biosciences and Engineering |
Print ISSN | 1547-1063 |
Publisher | American Institute of Mathematical Sciences |
Peer Reviewed | Peer Reviewed |
Volume | 19 |
Issue | 10 |
Pages | 10550-10580 |
DOI | https://doi.org/10.3934/mbe.2022493 |
Keywords | ensemble learning, intrusion detection, IoT, sensors security, ToN-IoT, bagging |
Public URL | http://researchrepository.napier.ac.uk/Output/2894905 |
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
(c)2022 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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