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Choosing machine learning algorithms for anomaly dection in smart builidng Iot scenarios
Date
2019
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.
Supervisor
Description
peer-reviewed
Publisher
IEEE Computer Society
Citation
2019 IEEE 5th World Forum on Internet of Things (WF-IoT);
Files
Keywords
Funding code
Funding Information
Science Foundation Ireland (SFI)
Sustainable Development Goals
External Link
Type
Meetings and Proceedings
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/