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
TypeArticle
TitleLearning to detect anomalous wireless links in IoT networks
AuthorsCerar, 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.

KeywordsWireless 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 Goals11 Sustainable cities and communities
Middlesex University ThemeSustainability
PublisherIEEE
JournalIEEE Access
ISSN
Electronic2169-3536
Publication dates
Online19 Nov 2020
Print08 Dec 2020
Publication process dates
Submitted03 Nov 2020
Accepted16 Nov 2020
Deposited15 Apr 2024
Output statusPublished
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 identifierWOS:000597200800001
LanguageEnglish
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