Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network

Tyler Cultice, Dan Ionel, Himanshu Thapliyal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home.

Original languageEnglish
Title of host publicationProceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020
Pages67-70
Number of pages4
ISBN (Electronic)9780738142647
DOIs
StatePublished - Dec 2020
Event6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 - Virtual, Chennai, India
Duration: Dec 14 2020Dec 16 2020

Publication series

NameProceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020

Conference

Conference6th IEEE International Symposium on Smart Electronic Systems, iSES 2020
Country/TerritoryIndia
CityVirtual, Chennai
Period12/14/2012/16/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Anomaly Detection
  • Cybersecurity
  • Machine Learning
  • Sensors
  • Smart Home

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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