Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios

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Title: Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios
Authors: Almaguer-Angeles, FernandoMurphy, JohnMurphy, LiamPortillo-Dominguez, A. Omar
Permanent link: http://hdl.handle.net/10197/10952
Date: 18-Apr-2019
Online since: 2019-07-31T08:30:41Z
Abstract: 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.
Funding Details: European Commission - European Regional Development Fund
Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Start page: 491
End page: 495
Copyright (published version): 2019 IEEE
Keywords: MeasurementInternet of ThingsTrainingAnomaly detectionMachine learning algorithmsLibrariesSmart buildings
DOI: 10.1109/wf-iot.2019.8767357
SFI/13/RC/2094
Other versions: http://wfiot2019.iot.ieee.org/
Language: en
Status of Item: Not peer reviewed
Is part of: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
Conference Details: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT'19)2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 15-18 April 2019
Appears in Collections:Computer Science Research Collection

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