A framework for machine learning based anomaly detection in Internet of Things data analysis
|Title:||A framework for machine learning based anomaly detection in Internet of Things data analysis||Authors:||Almaguer-Angeles, Fernando||Permanent link:||http://hdl.handle.net/10197/11656||Date:||2020||Online since:||2020-11-04T05:30:01Z||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, I compare the classification performance of 22 state-of-the-art supervised ML classification algorithms on different IoT smart-buildings datasets, when applied to the problem of anomaly detection. The results show that there is a set of ML algorithms that fits almost perfectly a type of datasets. Based on these results and a characterization of our datasets, this thesis propose a recommendation framework which guides practitioners towards the subset of the 22 ML algorithms which is likely to perform best on their data.||Type of material:||Master Thesis||Publisher:||University College Dublin. School of Computer Science||Qualification Name:||M.Sc.||Copyright (published version):||2020 the Author||Keywords:||Machine learning; Data analysis; Anomaly detection; Frameworks||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Theses|
Show full item record
This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.