Paper Info
Title
Choosing Machine Learning Algorithms for Anomaly Detection in Smart Building IoT Scenarios
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.
Year
DOI
Venue
2019
10.1109/WF-IoT.2019.8767357
2019 IEEE 5th World Forum on Internet of Things (WF-IoT)
Keywords
Field
DocType
anomaly detection,smart building IoT scenarios,log files,ML classification algorithms,machine learning,Internet of things
Anomaly detection,Computer science,Internet of Things,Raw data,Algorithm,Artificial intelligence,Building automation,Statistical classification,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-5386-4981-7
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Fernando Almaguer-Angeles100.34
John Murphy24013.87
Liam Murphy381174.94
A. Omar Portillo-Dominguez4255.68