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

TL;DR

This paper presents a deep learning-based anomaly detection system integrated into 3GPP IoT architecture, using autoencoders at device and network levels to improve security and management in cellular IoT deployments.

Contribution

It introduces a novel DL-based anomaly detection architecture with autoencoders at device and network levels for cellular IoT, validated through a real-world testbed.

Findings

Effective anomaly detection at device and network levels

Successful deployment within a 3GPP NB-IoT network

Balanced responsiveness and accuracy achieved

Abstract

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.