An anomaly detection study for the smart home environment
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/embargoedAccess
Özet
Unusual sensor data in smart homes may herald different problems based on sensor errors, security vulnerabilities, activity and behavior changes. This study focuses on detecting anomalies and unusual situations in 7 different sensor data in a house. For this, a model created with a combination of unsupervised and supervised machine learning algorithms is used. The sensor data are labeled using Isolation Forest which is one of the unsupervised algorithms. Then, the data is trained with the supervised algorithms Decision Tree, Extra Trees, Random Forest and XGBoost classification algorithms. Anomaly decisions are made with an accuracy of over 99 percent.
Açıklama
Anahtar Kelimeler
Machine Learning, Anomaly Detection, Smart Home, Edge Computing, IoT
Kaynak
Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022
WoS Q DeÄŸeri
Scopus Q DeÄŸeri
N/A
