Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network

Tyler Cultice; Dan Ionel; Himanshu Thapliyal

doi:10.1109/iSES50453.2020.00026

Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network

Tyler Cultice
, Dan Ionel
, Himanshu Thapliyal

Producción científica: Conference contribution › revisión exhaustiva

20 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020
Páginas	67-70
Número de páginas	4
ISBN (versión digital)	9780738142647
DOI	https://doi.org/10.1109/iSES50453.2020.00026
Estado	Published - dic 2020
Evento	6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 - Virtual, Chennai, India Duración: dic 14 2020 → dic 16 2020

Serie de la publicación

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

Conference

Conference	6th IEEE International Symposium on Smart Electronic Systems, iSES 2020
País/Territorio	India
Ciudad	Virtual, Chennai
Período	12/14/20 → 12/16/20

Nota bibliográfica

Publisher Copyright:
© 2020 IEEE.

Financiación

This research was supported by University of Kentucky ECE Undergraduate Fellowship Award by Schneider Electric.

Financiadores
University of Kentucky

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Affordable and clean energy

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

Acceder al documento

10.1109/iSES50453.2020.00026

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Cultice, T., Ionel, D., & Thapliyal, H. (2020). Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network. En Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 (pp. 67-70). Artículo 9426175 (Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020). https://doi.org/10.1109/iSES50453.2020.00026

@inproceedings{ce8ecc2660bf4d6a8ab4de7937e54490,

title = "Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network",

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.",

keywords = "Anomaly Detection, Cybersecurity, Machine Learning, Sensors, Smart Home",

author = "Tyler Cultice and Dan Ionel and Himanshu Thapliyal",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020 ; Conference date: 14-12-2020 Through 16-12-2020",

year = "2020",

month = dec,

doi = "10.1109/iSES50453.2020.00026",

language = "English",

series = "Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020",

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Cultice, T, Ionel, D & Thapliyal, H 2020, Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network. En Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020., 9426175, Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020, pp. 67-70, 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020, Virtual, Chennai, India, 12/14/20. https://doi.org/10.1109/iSES50453.2020.00026

Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network. / Cultice, Tyler; Ionel, Dan; Thapliyal, Himanshu.
Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020. 2020. p. 67-70 9426175 (Proceedings - 2020 6th IEEE International Symposium on Smart Electronic Systems, iSES 2020).

Producción científica: Conference contribution › revisión exhaustiva

TY - GEN

T1 - Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network

AU - Cultice, Tyler

AU - Ionel, Dan

AU - Thapliyal, Himanshu

PY - 2020/12

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N2 - 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.

AB - 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.

KW - Anomaly Detection

KW - Cybersecurity

KW - Machine Learning

KW - Sensors

KW - Smart Home

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DO - 10.1109/iSES50453.2020.00026

M3 - Conference contribution

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

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ER -

Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ODS de las Naciones Unidas

ASJC Scopus subject areas

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