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Choosing machine learning algorithms for anomaly dection in smart builidng Iot scenarios

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conference contribution
posted on 2020-01-22, 11:14 authored by Fernando Almaguer-Angeles, John Murphy, Liam Murphy, Andrés Omar Portillo-Domínguez
Internet of Things (IoT) systems produce large amounts of raw data in the form of log files. This raw data must then be processed to extract useful information. Machine Learning (ML) has proved to be an efficient technique for such tasks, but there are many different ML algorithms available, each suited to different types of scenarios. In this work, we compare the performance of 22 state-of-the-art supervised ML classification algorithms on different IoT datasets, when applied to the problem of anomaly detection. Our results show that there is no dominant solution, and that for each scenario, several candidate techniques perform similarly. Based on our results and a characterization of our datasets, we propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.

History

Publication

2019 IEEE 5th World Forum on Internet of Things (WF-IoT);

Publisher

IEEE Computer Society

Note

peer-reviewed

Other Funding information

SFI

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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Language

English

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