Learning to detect anomalous wireless links in IoT networks
Article
Cerar, G., Yetgin, H., Bertalanic, B. and Fortuna, C. 2020. Learning to detect anomalous wireless links in IoT networks. IEEE Access. 8, pp. 212130-212155. https://doi.org/10.1109/ACCESS.2020.3039333
Type | Article |
---|---|
Title | Learning to detect anomalous wireless links in IoT networks |
Authors | Cerar, G., Yetgin, H., Bertalanic, B. and Fortuna, C. |
Abstract | After decades of research, Internet of Things (IoT) is finally permeating real-life and helps improve the efficiency of infrastructures and processes as well as our health. As massive number of IoT devices are deployed, they naturally incurs great operational costs to ensure intended operations. To effectively handle such intended operations in massive IoT networks, automatic detection of malfunctioning, namely anomaly detection, becomes a critical but challenging task. In this paper, motivated by a real-world experimental IoT deployment, we introduce four types of wireless network anomalies that are identified at the link layer. We study the performance of threshold- and machine learning (ML)-based classifiers to automatically detect these anomalies. We examine the relative performance of three supervised and three unsupervised ML techniques on both non-encoded and encoded (autoencoder) feature representations. Our results demonstrate that; i) selected supervised approaches are able to detect anomalies with F1 scores of above 0.98, while unsupervised ones are also capable of detecting the said anomalies with F1 scores of, on average, 0.90, and ii) OC-SVM outperforms all the other unsupervised ML approaches reaching at F1 scores of 0.99 for SuddenD, 0.95 for SuddenR, 0.93 for InstaD and 0.95 for SlowD. |
Keywords | Wireless sensor networks; Anomaly detection; Wireless communication; Wireless networks; Internet of Things; Sensors; Monitoring; Anomaly detection; Internet of Things (IoT); machine learning (ML); wireless links; wireless networks |
Sustainable Development Goals | 11 Sustainable cities and communities |
Middlesex University Theme | Sustainability |
Publisher | IEEE |
Journal | IEEE Access |
ISSN | |
Electronic | 2169-3536 |
Publication dates | |
Online | 19 Nov 2020 |
08 Dec 2020 | |
Publication process dates | |
Submitted | 03 Nov 2020 |
Accepted | 16 Nov 2020 |
Deposited | 15 Apr 2024 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2020.3039333 |
Web of Science identifier | WOS:000597200800001 |
Language | English |
https://repository.mdx.ac.uk/item/11z152
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Publisher's version
Learning_to_Detect_Anomalous_Wireless_Links_in_IoT_Networks.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
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