  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.
