Name
Abstract | ||
|---|---|---|
In this paper we study anomaly detection methods for home IoT devices. Specifically, we address unsupervised one-class learning methods due to their ability to learn deviations from a single normal class. In a home IoT environment, this consideration is crucial as supervised methods would result in a burden on many non-technical consumers which could hinder their effectiveness. For our study, we d... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CyberSA52016.2021.9478248 | 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) |
Keywords | DocType | ISBN |
Learning systems,Data analysis,Tools,Anomaly detection,Monitoring | Conference | 978-1-6654-2529-2 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan White | 1 | 44 | 6.49 |
| Phil Legg | 2 | 1 | 2.05 |