Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics

The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate. This poses significant device management and security challenges to current IoT networks. Among several approaches to cope with these challenges, data-based methods root...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.59406-59419
Main Authors: Savic, Milos, Lukic, Milan, Danilovic, Dragan, Bodroski, Zarko, Bajovic, Dragana, Mezei, Ivan, Vukobratovic, Dejan, Skrbic, Srdjan, Jakovetic, Dusan
Format: Article
Language:English
Subjects:
3GPP
Anomalies
Anomaly detection
cellular IoT
Computer architecture
Deep learning
Image edge detection
Industrial Internet of Things
industrial IoT
Internet of Things
Logic gates
Logistics
machine learning
smart logistics
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Summary:The number of connected Internet of Things (IoT) devices within cyber-physical infrastructure systems grows at an increasing rate. This poses significant device management and security challenges to current IoT networks. Among several approaches to cope with these challenges, data-based methods rooted in deep learning (DL) are receiving an increased interest. In this paper, motivated by the upcoming surge of 5G IoT connectivity in industrial environments, we propose to integrate a DL-based anomaly detection (AD) as a service into the 3GPP mobile cellular IoT architecture. The proposed architecture embeds autoencoder based anomaly detection modules both at the IoT devices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancing between the system responsiveness and accuracy. We design, integrate, demonstrate and evaluate a testbed that implements the above service in a real-world deployment integrated within the 3GPP Narrow-Band IoT (NB-IoT) mobile operator network.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3072916