Deep Learning Anomaly Detection for Cellular IoT With Applications in Smart Logistics
| Autor: | Dusan Jakovetic, Zarko Bodroski, Dejan Vukobratovic, Dragan Danilovic, Srdjan Skrbic, Dragana Bajovic, Ivan Mezei, Miloš Savić, Milan Lukic |
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| Jazyk: | angličtina |
| Předmět: |
FOS: Computer and information sciences
Service (systems architecture) General Computer Science Computer science Core network 02 engineering and technology Anomaly detection Cellular IoT Computer Science - Networking and Internet Architecture Industrial IoT Machine learning 0202 electrical engineering electronic engineering information engineering General Materials Science Smart logistics Networking and Internet Architecture (cs.NI) business.industry Deep learning 020208 electrical & electronic engineering Testbed General Engineering 020206 networking & telecommunications Autoencoder TK1-9971 Software deployment Artificial intelligence Electrical engineering. Electronics. Nuclear engineering business 5G Computer network |
| Zdroj: | IEEE Access, Vol 9, Pp 59406-59419 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3072916 |
| Popis: | 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. Comment: 12 pages, 9 figures, revised version, submitted to IEEE journal for possible publication |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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