Paper Info
Title
Smart Home Iot Anomaly Detection Based On Ensemble Model Learning From Heterogeneous Data
Abstract
Nowadays, internet based home automation is made possible with the advent of intelligent device control. These electronic sensing devices transfer an enormous amount of data into the cloud. It is a challenge to discover hidden information from the massive amount of stored data in the cloud. In addition, privacy, security, and stability could also be a concern for users. Due to these issues becoming ever more prevalent in today's society, the need to have access to readily anomaly detection becomes crucial for the modern smart home user.In this paper, we design, test and evaluate an ensemble model anomaly detection method. Our method targets the data anomalies present in general smart Internet of Things (IoT) devices, allowing for easy detection of anomalous events based on stored data. We make our method robust through ensemble machine learning model training. We aim to simulate different types of anomaly situations on publicly available smart home data sets, thereby exposing our models to likely real world phenomenons and events that may cause anomalies. Experiments are conducted on the processed data and evaluated for accuracy through validation and testing against independent and identically distributed labeled data.
Year
DOI
Venue
2019
10.1109/BigData47090.2019.9006249
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Keywords
Field
DocType
Internet of Things, smart home devices, anomaly detection, ensemble model, data analysis
Data mining,Anomaly detection,Data set,Ensemble forecasting,Computer science,Home automation,Artificial intelligence,Independent and identically distributed random variables,Ensemble learning,Machine learning,Cloud computing,The Internet
Conference
ISSN
Citations 
PageRank 
2639-1589
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Sihai Tang1164.54
Zhaochen Gu201.35
Qing Yang328430.11
Song Fu444835.66