Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LA-CCI.2018.8625228 | 2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI) |
Keywords | Field | DocType |
Anomaly Detection,Internet of Things,Machine Learning,Comparative Study | Anomaly detection,Computer science,Internet of Things,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-4627-4 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shane Brady | 1 | 0 | 0.34 |
| Damien Magoni | 2 | 240 | 30.36 |
| John Murphy | 3 | 597 | 52.43 |
| Haytham Assem | 4 | 21 | 6.00 |
| A. Omar Portillo-Dominguez | 5 | 25 | 5.68 |