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AnoML-IoT: an end to end re-configurable multi-protocol anomaly detection pipeline for Internet of Things

Kayan, Hakan, Majib, Yasar, Alsafery, Wael, Barhamgi, Mahmoud and Perera, Charith ORCID: 2021. AnoML-IoT: an end to end re-configurable multi-protocol anomaly detection pipeline for Internet of Things. Internet of Things 16 , 100437. 10.1016/j.iot.2021.100437

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The rapid development in ubiquitous computing has enabled the use of microcontrollers as edge devices. These devices are used to develop truly distributed IoT-based mechanisms where machine learning (ML) models are utilized. However, integrating ML models to edge devices requires an understanding of various software tools such as programming languages and domain-specific knowledge. Anomaly detection is one of the domains where a high level of expertise is required to achieve promising results. In this work, we present AnoML which is an end-to-end data science pipeline that allows the integration of multiple wireless communication protocols, anomaly detection algorithms, deployment to the edge, fog, and cloud platforms with minimal user interaction. We facilitate the development of IoT anomaly detection mechanisms by reducing the barriers that are formed due to the heterogeneity of an IoT environment. The proposed pipeline supports four main phases: (i) data ingestion, (ii) model training, (iii) model deployment, (iv) inference and maintaining. We evaluate the pipeline with two anomaly detection datasets while comparing the efficiency of several machine learning algorithms within different nodes. We also provide the source code of the developed tools which are the main components of the pipeline.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Elsevier
ISSN: 2542-6605
Date of First Compliant Deposit: 28 January 2022
Date of Acceptance: 13 July 2021
Last Modified: 07 Nov 2023 05:06

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