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Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios
Almaguer-Angeles, Fernando; Murphy, John; Murphy, Liam, B.E.; Portillo Dominguez, Andres Omar
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 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. European Commission - European Regional Development Fund Science Foundation Ireland
Keyword(s): Measurement; Internet of Things; Training; Anomaly detection; Machine learning algorithms; Libraries; Smart buildings
Publication Date:
Type: Other
Peer-Reviewed: Unknown
Language(s): English
Institution: University College Dublin
Publisher(s): IEEE
First Indexed: 2019-08-02 06:15:23 Last Updated: 2020-02-29 06:17:42