An anomaly detection study for the smart home environment
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Access Rights
info:eu-repo/semantics/embargoedAccess
Abstract
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.
Description
Keywords
Machine Learning, Anomaly Detection, Smart Home, Edge Computing, IoT
Journal or Series
Proceedings - 7th International Conference on Computer Science and Engineering, UBMK 2022
WoS Q Value
Scopus Q Value
N/A
