Naz, N., Khan, M. A., Alsuhibany, S. A., Diyan, M., Tan, Z., Khan, M. A. and Ahmad, J. (2022) Ensemble learning-based IDS for sensors telemetry data in IoT networks. Mathematical Biosciences and Engineering, 19(10), pp. 10550-10580. (doi: 10.3934/mbe.2022493) (PMID:36032006)
Text
276244.pdf - Published Version Available under License Creative Commons Attribution. 1MB |
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.
Item Type: | Articles |
---|---|
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Diyan, Dr Muhammad |
Authors: | Naz, N., Khan, M. A., Alsuhibany, S. A., Diyan, M., Tan, Z., Khan, M. A., and Ahmad, J. |
College/School: | College of Science and Engineering > School of Physics and Astronomy |
Journal Name: | Mathematical Biosciences and Engineering |
Publisher: | American Institute of Mathematical Sciences |
ISSN: | 1547-1063 |
ISSN (Online): | 1551-0018 |
Published Online: | 25 July 2022 |
Copyright Holders: | Copyright © 2022 The Authors |
First Published: | First published in Mathematical Biosciences and Engineering 19(10): 10550-10580 |
Publisher Policy: | Reproduced under a Creative Commons License |
University Staff: Request a correction | Enlighten Editors: Update this record