@inproceedings{9e6020e4ce02477ca75e39c7130c1f5f,
title = "Analysis of Machine Learning Techniques for Anomaly Detection in the Internet of Things",
abstract = "A major challenge faced in the Internet of Things (IoT) is discovering issues that can occur in it, such as anomalies in the network or within the IoT devices. The nature of IoT hinders the identification of issues because of the huge number of devices and amounts of data generated. The aim of this paper is to investigate machine learning for effectively identifying anomalies in an IoT environment. We evaluated several state-of-the-art techniques which can identify, in real-time, when anomalies have occurred, allowing users to make alterations to the IoT network to eliminate the anomalies. Our results offer practitioners a valuable reference about which techniques might be more appropriate for their usage scenarios.",
keywords = "Anomaly Detection, Comparative Study, Internet of Things, Machine Learning",
author = "Shane Brady and Damien Magoni and John Murphy and Haytham Assem and Portillo-Dominguez, \{A. Omar\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018 ; Conference date: 06-11-2018 Through 09-11-2018",
year = "2019",
month = jan,
day = "23",
doi = "10.1109/LA-CCI.2018.8625228",
language = "English",
series = "2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018",
address = "United States",
}