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The number of connected Internet of Things (IoT) devices withincyber-physical infrastructure systems grows at an increasing rate. This posessignificant device management and security challenges to current IoT networks.Among several approaches to cope with these challenges, data-based methodsrooted in deep learning (DL) are receiving an increased interest. In thispaper, motivated by the upcoming surge of 5G IoT connectivity in industrialenvironments, we propose to integrate a DL-based anomaly detection (AD) as aservice into the 3GPP mobile cellular IoT architecture. The proposedarchitecture embeds autoencoder based anomaly detection modules both at the IoTdevices (ADM-EDGE) and in the mobile core network (ADM-FOG), thereby balancingbetween the system responsiveness and accuracy. We design, integrate,demonstrate and evaluate a testbed that implements the above service in areal-world deployment integrated within the 3GPP Narrow-Band IoT (NB-IoT)mobile operator network.

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